{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Downloading data from WorldPop.org" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "from epigraphhub.data.worldpop import WorldPop\n", "from IPython.display import display, Markdown, Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get started we instantiate a WorldPop object to Consume the API." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "WP = WorldPop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The representation of this object is a markdown table describing the contents of the API." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/markdown": [ "| alias | name | title | desc |\n", "|:---------------------|:----------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", "| pop | Population Counts | Population Counts | WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. |\n", "| | | | Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. |\n", "| | | |

|\n", "| | | | Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. |\n", "| | | | These are 100m resolution gridded population estimates using customized methods (\"bottom-up\" and/or \"top-down\") developed for the latest data available from each country. |\n", "| | | | They can also be visualised and explored through the woprVision App. |\n", "| | | |
|\n", "| | | | The remaining datasets in the links below are produced using the \"top-down\" method, |\n", "| | | | with either the unconstrained or constrained top-down disaggregation method used. |\n", "| | | | Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. |\n", "| | | | Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): |\n", "| | | |

|\n", "| | | | - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using |\n", "| | | | unconstrained top-down methods for all countries of the World for each year 2000-2020. |\n", "| | | |
|\n", "| | | | - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using |\n", "| | | | unconstrained top-down methods for all countries of the World for each year 2000-2020. |\n", "| | | |
|\n", "| | | | - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using |\n", "| | | | unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) |\n", "| | | |
|\n", "| | | | -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using |\n", "| | | | unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). |\n", "| | | |
|\n", "| | | | -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the \"Unconstrained individual countries 2000-2020\" datasets. |\n", "| | | |
|\n", "| | | | -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using |\n", "| | | | constrained top-down methods for all countries of the World for 2020. |\n", "| | | |
|\n", "| | | | -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using |\n", "| | | | constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national |\n", "| | | | population estimates (UN 2019). |\n", "| | | |
|\n", "| | | |
|\n", "| | | | Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing \"top-down\" methods and time periods are still available for download here: Individual countries and Whole Continent. |\n", "| births | Births | Births | The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al.. |\n", "| pregnancies | Pregnancies | Pregnancies | The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al.. |\n", "| urban_change | Urban change | Urban change | East–Southeast Asia is currently one of the fastest urbanizing regions in the world, with countries such as China climbing from 20 to 50% urbanized in just a few decades. However, spatially-and temporally-detailed information on regional-scale changes in urban land or population distribution have not previously been available; previous efforts have been either sample-based, focused on one country, or drawn conclusions from datasets with substantial temporal/spatial mismatch and variability in urban definitions. In collaboration with the World Bank and University of Wisconsin-Madison, WorldPop used consistent methodology, satellite imagery and census data for >1000 agglomerations in the East–Southeast Asian region to map population changes between 2000 and 2010. The data are available here and described in detail in Schneider et al, and this report. |\n", "| age_structures | Age and sex structures | Age and sex structures | WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. |\n", "| | | | Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. |\n", "| | | |

|\n", "| | | | |\n", "| | | | A description of the modelling methods used for age and sex structures can be found in |\n", "| | | | |\n", "| | | | Tatem et al and |\n", "| | | | Pezzulo et al. Details of the input population count datasets used can be found here, and age/sex structure proportion datasets here. |\n", "| | | |
|\n", "| | | | Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined |\n", "| | | | here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020. |\n", "| | | | The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping. |\n", "| dahi | Development Indicators | Development and Health Indicators | Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. WorldPop develops methods for the integration of geolocated cluster sample data from household surveys with geospatial covariates in Bayesian geostatistical modelling frameworks to map key development and health indicators at high spatial resolution. These include indicators relating to poverty, literacy, sanitation, maternal and newborn health, contraceptive use and vaccination coverage, among others. Details of methods used and outputs can be found in Utazi et al 1, Utazi et al 2, Steele et al, Bosco et al, Ruktanonchai et al and Tatem et al. |\n", "| dependency_ratios | Dependency Ratios | Dependency Ratios | The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country’s economic prospects and health burdens. WorldPop’s assembly of subnational population pyramid data has here been used to produce continent-wide subnational scale dependency ratio datasets, with full details found in Pezzulo et al. |\n", "| internal_migration_f | Migration Flows | Migration Flows | Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for |\n", "| | | | disease control policy, economic development, and resource allocation. WorldPop has worked to pair census microdata from multiple low and middle income countries with migrant stock data and additional |\n", "| | | | geospatial datasets to develop models for internal and international migration flows, |\n", "| | | | including key drivers that reflect the changing social, demographic, economic, and |\n", "| | | | environmental landscapes. These have been applied to map internal and international migration at sub-national level for all low and middle income countries, with the datasets available to download here, and methods described in Sorichetta et al, Garcia et al, Ceausu et al., Modelling sex-disaggregated internal migration flows in low- and middle-income countries (in preparation), and Ceausu et al., Estimating sex-disaggregated interregional migration in the Global South (in preparation). |\n", "| dynamic_mapping | Dynamic Mapping | Dynamic Mapping | Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. WorldPop has demonstrated how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. Methods are described in Deville et al and zu Erbach-Schoenberg et al, and datasets representing estimated monthly population distributions for France and Portugal are available to download here. |\n", "| global_flight_data | Global Flight Data | Global Flight Data | The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers and analysts to rely on scheduled flight seat capacity data or simple models of flow. WorldPop has collated openly available monthly statistics of air passengers across countries and years, and developed models of global air passenger flows, with annual flow methods described in Huang et al, seasonal flows described in Mao et al, and the dataset collected and output estimates available to download here. |\n", "| covariates | Covariates | Geospatial covariate data layers | Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end WorldPop has produced an open access archive of 3 and 30 arc-second resolution gridded data, which are available to download here and are described further in Lloyd et al 2019 and also Lloyd et al 2017. |\n", "| gbsg | Global Settlement Growth | Global Built-Settlement Growth | Changing populations are often accompanied by changing built-settlement landscapes. Here, small area population data and a limited set of environmental covariates have been combined with machine learning methods and dynamically-limited growth curves to annually interpolate (from 2000 to 2014) and annually project (from 2015 to 2020) the presence of built-settlements across the globe at 100m resolution. These annual built-settlement maps were then used to inform the WorldPop \"Global per country 2000-2020\" population datasets. An overview of the built-settlement growth modeling framework can be found in Nieves et al. |\n", "| gridcellsurfaceareas | Grid-cell surface areas | Grid-cell surface areas | Grid-cell surface areas |\n", "| adminareas | Administrative Areas | Administrative Areas | Administrative Areas |\n", "| pop_density | Population Density | Population Density | WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. |\n", "| | | | Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. |\n", "| | | |

|\n", "| | | | Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) |\n", "| | | |
|\n", "| | | | |\n", "| | | | -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding |\n", "| | | | Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. |\n", "| | | | These are produced using the unconstrained top-down modelling method. |\n", "| | | |
|\n", "| | | | -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding |\n", "| | | | Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, |\n", "| | | | adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. |\n", "| | | | These are produced using the unconstrained top-down modelling method. |\n", "| pwd | Population Weighted Density | Population Weighted Density | Population Weighted Density (PWD) is an alternative to conventional approaches to population density that is arguably better suited to some types of research in the fields of social science and epidemiology. In this release WorldPop publishes what we believe may be the first set of global estimates for PWD, which we offer at national and subnational levels since 2000. |\n", "| | | | Please make sure you have read our Population Weighted Density overview page before choosing and downloading a dataset. |" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(f\"{WP}\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading table lists\n", "\n", "Once we know which dataset we want to explore, for example, *population(pop)*, we can list all the tables available within it by organizational level:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/markdown": [ "### Individual countries: *pic*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitlepopyeariso3
01Armenia 100m PopulationNoneARM
12Azerbaijan 100m PopulationNoneAZE
23Iraq 100m PopulationNoneIRQ
34Saudi Arabia 100m PopulationNoneSAU
45Uzbekistan 100m PopulationNoneUZB
...............
134135Bhutan 100m PopulationNoneBTN
135136State of Palestine 100m PopulationNonePSE
136137Ukraine 100m PopulationNoneUKR
137138Kazakhstan 100m PopulationNoneKAZ
13817044Nigeria 100m PopulationNoneNGA
\n", "

139 rows × 4 columns

\n", "
" ], "text/plain": [ " id title popyear iso3\n", "0 1 Armenia 100m Population None ARM\n", "1 2 Azerbaijan 100m Population None AZE\n", "2 3 Iraq 100m Population None IRQ\n", "3 4 Saudi Arabia 100m Population None SAU\n", "4 5 Uzbekistan 100m Population None UZB\n", ".. ... ... ... ...\n", "134 135 Bhutan 100m Population None BTN\n", "135 136 State of Palestine 100m Population None PSE\n", "136 137 Ukraine 100m Population None UKR\n", "137 138 Kazakhstan 100m Population None KAZ\n", "138 17044 Nigeria 100m Population None NGA\n", "\n", "[139 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Whole Continent: *pop_continent*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitlepopyeariso3
0139Africa Continental Population Datasets (2000 -...NoneWCT
1140Asia Continental Population Datasets (2000 - 2...NoneWCA
2141Latin America and the Caribbean Continental Po...NoneWCB
\n", "
" ], "text/plain": [ " id title popyear iso3\n", "0 139 Africa Continental Population Datasets (2000 -... None WCT\n", "1 140 Asia Continental Population Datasets (2000 - 2... None WCA\n", "2 141 Latin America and the Caribbean Continental Po... None WCB" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Unconstrained individual countries 2000-2020 ( 100m resolution ): *wpgp*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitlepopyeariso3
01325The spatial distribution of population in 2000...2000AUS
11326The spatial distribution of population in 2000...2000RUS
21327The spatial distribution of population in 2000...2000BRA
31328The spatial distribution of population in 2000...2000CAN
41329The spatial distribution of population in 2000...2000CZE
...............
52166541The spatial distribution of population in 2020...2020ATF
52176542The spatial distribution of population in 2020...2020HMD
52186543The spatial distribution of population in 2020...2020UMI
52196544The spatial distribution of population in 2020...2020SPR
52206545The spatial distribution of population in 2020...2020USA
\n", "

5221 rows × 4 columns

\n", "
" ], "text/plain": [ " id title popyear iso3\n", "0 1325 The spatial distribution of population in 2000... 2000 AUS\n", "1 1326 The spatial distribution of population in 2000... 2000 RUS\n", "2 1327 The spatial distribution of population in 2000... 2000 BRA\n", "3 1328 The spatial distribution of population in 2000... 2000 CAN\n", "4 1329 The spatial distribution of population in 2000... 2000 CZE\n", "... ... ... ... ...\n", "5216 6541 The spatial distribution of population in 2020... 2020 ATF\n", "5217 6542 The spatial distribution of population in 2020... 2020 HMD\n", "5218 6543 The spatial distribution of population in 2020... 2020 UMI\n", "5219 6544 The spatial distribution of population in 2020... 2020 SPR\n", "5220 6545 The spatial distribution of population in 2020... 2020 USA\n", "\n", "[5221 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Unconstrained global mosaics 2000-2020 ( 1km resolution ): *wpgp1km*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitlepopyeariso3
024757The spatial distribution of population in 20002000None
124758The spatial distribution of population in 20012001None
224759The spatial distribution of population in 20022002None
324760The spatial distribution of population in 20032003None
424761The spatial distribution of population in 20042004None
524762The spatial distribution of population in 20052005None
624763The spatial distribution of population in 20062006None
724764The spatial distribution of population in 20072007None
824765The spatial distribution of population in 20082008None
924766The spatial distribution of population in 20092009None
1024767The spatial distribution of population in 20102010None
1124768The spatial distribution of population in 20112011None
1224769The spatial distribution of population in 20122012None
1324770The spatial distribution of population in 20132013None
1424771The spatial distribution of population in 20142014None
1524772The spatial distribution of population in 20152015None
1624773The spatial distribution of population in 20162016None
1724774The spatial distribution of population in 20172017None
1824775The spatial distribution of population in 20182018None
1924776The spatial distribution of population in 20192019None
2024777The spatial distribution of population in 20202020None
\n", "
" ], "text/plain": [ " id title popyear iso3\n", "0 24757 The spatial distribution of population in 2000 2000 None\n", "1 24758 The spatial distribution of population in 2001 2001 None\n", "2 24759 The spatial distribution of population in 2002 2002 None\n", "3 24760 The spatial distribution of population in 2003 2003 None\n", "4 24761 The spatial distribution of population in 2004 2004 None\n", "5 24762 The spatial distribution of population in 2005 2005 None\n", "6 24763 The spatial distribution of population in 2006 2006 None\n", "7 24764 The spatial distribution of population in 2007 2007 None\n", "8 24765 The spatial distribution of population in 2008 2008 None\n", "9 24766 The spatial distribution of population in 2009 2009 None\n", "10 24767 The spatial distribution of population in 2010 2010 None\n", "11 24768 The spatial distribution of population in 2011 2011 None\n", "12 24769 The spatial distribution of population in 2012 2012 None\n", "13 24770 The spatial distribution of population in 2013 2013 None\n", "14 24771 The spatial distribution of population in 2014 2014 None\n", "15 24772 The spatial distribution of population in 2015 2015 None\n", "16 24773 The spatial distribution of population in 2016 2016 None\n", "17 24774 The spatial distribution of population in 2017 2017 None\n", "18 24775 The spatial distribution of population in 2018 2018 None\n", "19 24776 The spatial distribution of population in 2019 2019 None\n", "20 24777 The spatial distribution of population in 2020 2020 None" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### WP00643: **" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
aliasname
0picIndividual countries
1pop_continentWhole Continent
2wpgpUnconstrained individual countries 2000-2020 (...
3wpgp1kmUnconstrained global mosaics 2000-2020 ( 1km r...
4WP00643
5wpgpunadjUnconstrained individual countries 2000-2020 U...
6wpic1kmUnconstrained individual countries 2000-2020 ...
7wpicuadj1kmUnconstrained individual countries 2000-2020 U...
8cic2020_100mConstrained Individual countries 2020 ( 100m r...
9cic2020_UNadj_100mConstrained Individual countries 2020 UN adjus...
\n", "
" ], "text/plain": [ " alias name\n", "0 pic Individual countries\n", "1 pop_continent Whole Continent\n", "2 wpgp Unconstrained individual countries 2000-2020 (...\n", "3 wpgp1km Unconstrained global mosaics 2000-2020 ( 1km r...\n", "4 WP00643\n", "5 wpgpunadj Unconstrained individual countries 2000-2020 U...\n", "6 wpic1km Unconstrained individual countries 2000-2020 ...\n", "7 wpicuadj1km Unconstrained individual countries 2000-2020 U...\n", "8 cic2020_100m Constrained Individual countries 2020 ( 100m r...\n", "9 cic2020_UNadj_100m Constrained Individual countries 2020 UN adjus..." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): *wpgpunadj*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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idtitlepopyeariso3
024801The spatial distribution of population in 2000...2000RUS
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224803The spatial distribution of population in 2002...2002RUS
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424805The spatial distribution of population in 2004...2004RUS
...............
488529686The spatial distribution of population in 2016...2016ZMB
488629687The spatial distribution of population in 2017...2017ZMB
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488829689The spatial distribution of population in 2019...2019ZMB
488929690The spatial distribution of population in 2020...2020ZMB
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4890 rows × 4 columns

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idtitlepopyeariso3
029693The spatial distribution of population in 2000...2000RUS
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329696The spatial distribution of population in 2003...2003RUS
429697The spatial distribution of population in 2004...2004RUS
...............
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idtitlepopyeariso3
034754The spatial distribution of population in 2000...2000RUS
134755The spatial distribution of population in 2001...2001RUS
234756The spatial distribution of population in 2002...2002RUS
334757The spatial distribution of population in 2003...2003RUS
434758The spatial distribution of population in 2004...2004RUS
...............
490939663The spatial distribution of population in 2016...2016ZMB
491039664The spatial distribution of population in 2017...2017ZMB
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491339667The spatial distribution of population in 2020...2020ZMB
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4914 rows × 4 columns

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idtitlepopyeariso3
049623The spatial distribution of population in 2020...2020AGO
149624The spatial distribution of population in 2020...2020BWA
249625The spatial distribution of population in 2020...2020BDI
349626The spatial distribution of population in 2020...2020CMR
449627The spatial distribution of population in 2020...2020CPV
...............
23549909The spatial distribution of population in 2020...2020VEN
23649910The spatial distribution of population in 2020...2020WLF
23749911The spatial distribution of population in 2020...2020WSM
23849912The spatial distribution of population in 2020...2020YEM
23949913The spatial distribution of population in 2020...2020KOS
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240 rows × 4 columns

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" ], "text/plain": [ " id title popyear iso3\n", "0 49623 The spatial distribution of population in 2020... 2020 AGO\n", "1 49624 The spatial distribution of population in 2020... 2020 BWA\n", "2 49625 The spatial distribution of population in 2020... 2020 BDI\n", "3 49626 The spatial distribution of population in 2020... 2020 CMR\n", "4 49627 The spatial distribution of population in 2020... 2020 CPV\n", ".. ... ... ... ...\n", "235 49909 The spatial distribution of population in 2020... 2020 VEN\n", "236 49910 The spatial distribution of population in 2020... 2020 WLF\n", "237 49911 The spatial distribution of population in 2020... 2020 WSM\n", "238 49912 The spatial distribution of population in 2020... 2020 YEM\n", "239 49913 The spatial distribution of population in 2020... 2020 KOS\n", "\n", "[240 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Constrained Individual countries 2020 UN adjusted (100m resolution): *cic2020_UNadj_100m*" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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idtitlepopyeariso3
049674The spatial distribution of population in 2020...2020AGO
149675The spatial distribution of population in 2020...2020BWA
249676The spatial distribution of population in 2020...2020BDI
349677The spatial distribution of population in 2020...2020CMR
449678The spatial distribution of population in 2020...2020CPV
...............
22950092The spatial distribution of population in 2020...2020UZB
23050093The spatial distribution of population in 2020...2020VEN
23150094The spatial distribution of population in 2020...2020WLF
23250095The spatial distribution of population in 2020...2020WSM
23350096The spatial distribution of population in 2020...2020YEM
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234 rows × 4 columns

\n", "
" ], "text/plain": [ " id title popyear iso3\n", "0 49674 The spatial distribution of population in 2020... 2020 AGO\n", "1 49675 The spatial distribution of population in 2020... 2020 BWA\n", "2 49676 The spatial distribution of population in 2020... 2020 BDI\n", "3 49677 The spatial distribution of population in 2020... 2020 CMR\n", "4 49678 The spatial distribution of population in 2020... 2020 CPV\n", ".. ... ... ... ...\n", "229 50092 The spatial distribution of population in 2020... 2020 UZB\n", "230 50093 The spatial distribution of population in 2020... 2020 VEN\n", "231 50094 The spatial distribution of population in 2020... 2020 WLF\n", "232 50095 The spatial distribution of population in 2020... 2020 WSM\n", "233 50096 The spatial distribution of population in 2020... 2020 YEM\n", "\n", "[234 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for df in WP.get_dataset_tables(\"pop\"):\n", " display(Markdown(f\"### {df['name']}: *{df['alias']}*\"))\n", " display(df['data'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `get_dataset_tables` is a generator function that returns dictionaries containing on dataframe and metadata at a time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can dig deeper to get closer to the actual data: we pick an area, say *population (pop)*, a level, say *Unconstrained individual countries 2000-2020 ( 1km resolution ): \"wpic1km\"* and the country: *Guyana*. This is possible through get_data_by_country." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "{'data': [{'id': '31604',\n", " 'title': 'The spatial distribution of population in 2000 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2000',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2000/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2000/GUY/guy_ppp_2000_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2000/GUY/guy_ppp_2000_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31604/guy_ppp_1km_2000_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31604'},\n", " {'id': '31605',\n", " 'title': 'The spatial distribution of population in 2001 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2001',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2001/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2001/GUY/guy_ppp_2001_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2001/GUY/guy_ppp_2001_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31605/guy_ppp_1km_2001_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31605'},\n", " {'id': '31606',\n", " 'title': 'The spatial distribution of population in 2002 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2002',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2002/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2002/GUY/guy_ppp_2002_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2002/GUY/guy_ppp_2002_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31606/guy_ppp_1km_2002_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31606'},\n", " {'id': '31607',\n", " 'title': 'The spatial distribution of population in 2003 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2003',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2003/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2003/GUY/guy_ppp_2003_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2003/GUY/guy_ppp_2003_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31607/guy_ppp_1km_2003_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31607'},\n", " {'id': '31608',\n", " 'title': 'The spatial distribution of population in 2004 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2004',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2004/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2004/GUY/guy_ppp_2004_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2004/GUY/guy_ppp_2004_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31608/guy_ppp_1km_2004_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31608'},\n", " {'id': '31609',\n", " 'title': 'The spatial distribution of population in 2005 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2005',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2005/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2005/GUY/guy_ppp_2005_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2005/GUY/guy_ppp_2005_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31609/guy_ppp_1km_2005_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31609'},\n", " {'id': '31610',\n", " 'title': 'The spatial distribution of population in 2006 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2006',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2006/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2006/GUY/guy_ppp_2006_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2006/GUY/guy_ppp_2006_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31610/guy_ppp_1km_2006_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31610'},\n", " {'id': '31611',\n", " 'title': 'The spatial distribution of population in 2007 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2007',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2007/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2007/GUY/guy_ppp_2007_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2007/GUY/guy_ppp_2007_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31611/guy_ppp_1km_2007_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31611'},\n", " {'id': '31612',\n", " 'title': 'The spatial distribution of population in 2008 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2008',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2008/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2008/GUY/guy_ppp_2008_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2008/GUY/guy_ppp_2008_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31612/guy_ppp_1km_2008_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31612'},\n", " {'id': '31613',\n", " 'title': 'The spatial distribution of population in 2009 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2009',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2009/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2009/GUY/guy_ppp_2009_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2009/GUY/guy_ppp_2009_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31613/guy_ppp_1km_2009_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31613'},\n", " {'id': '31614',\n", " 'title': 'The spatial distribution of population in 2010 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2010',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2010/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2010/GUY/guy_ppp_2010_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2010/GUY/guy_ppp_2010_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31614/guy_ppp_1km_2010_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31614'},\n", " {'id': '31615',\n", " 'title': 'The spatial distribution of population in 2011 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2011',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2011/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2011/GUY/guy_ppp_2011_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2011/GUY/guy_ppp_2011_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31615/guy_ppp_1km_2011_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31615'},\n", " {'id': '31616',\n", " 'title': 'The spatial distribution of population in 2012 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2012',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2012/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2012/GUY/guy_ppp_2012_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2012/GUY/guy_ppp_2012_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31616/guy_ppp_1km_2012_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31616'},\n", " {'id': '31617',\n", " 'title': 'The spatial distribution of population in 2013 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2013',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2013/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2013/GUY/guy_ppp_2013_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2013/GUY/guy_ppp_2013_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31617/guy_ppp_1km_2013_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31617'},\n", " {'id': '31618',\n", " 'title': 'The spatial distribution of population in 2014 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2014',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2014/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2014/GUY/guy_ppp_2014_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2014/GUY/guy_ppp_2014_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31618/guy_ppp_1km_2014_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31618'},\n", " {'id': '31619',\n", " 'title': 'The spatial distribution of population in 2015 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2015',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2015/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2015/GUY/guy_ppp_2015_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2015/GUY/guy_ppp_2015_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31619/guy_ppp_1km_2015_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31619'},\n", " {'id': '31620',\n", " 'title': 'The spatial distribution of population in 2016 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2016',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2016/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2016/GUY/guy_ppp_2016_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2016/GUY/guy_ppp_2016_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31620/guy_ppp_1km_2016_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31620'},\n", " {'id': '31621',\n", " 'title': 'The spatial distribution of population in 2017 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2017',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2017/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2017/GUY/guy_ppp_2017_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2017/GUY/guy_ppp_2017_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31621/guy_ppp_1km_2017_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31621'},\n", " {'id': '31622',\n", " 'title': 'The spatial distribution of population in 2018 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2018',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2018/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2018/GUY/guy_ppp_2018_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2018/GUY/guy_ppp_2018_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31622/guy_ppp_1km_2018_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31622'},\n", " {'id': '31623',\n", " 'title': 'The spatial distribution of population in 2019 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2019',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2019/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2019/GUY/guy_ppp_2019_1km_Aggregated.tif',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2019/GUY/guy_ppp_2019_1km_ASCII_XYZ.zip'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31623/guy_ppp_1km_2019_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31623'},\n", " {'id': '31624',\n", " 'title': 'The spatial distribution of population in 2020 Guyana',\n", " 'desc': 'Estimated total number of people per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.',\n", " 'doi': '10.5258/SOTON/WP00670',\n", " 'date': '2020-06-22',\n", " 'popyear': '2020',\n", " 'citation': 'WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00670',\n", " 'data_file': 'GIS/Population/Global_2000_2020_1km/2020/GUY',\n", " 'archive': 'N',\n", " 'public': 'Y',\n", " 'source': 'WorldPop, University of Southampton, UK',\n", " 'data_format': 'Geotiff',\n", " 'author_email': 'info@worldpop.org',\n", " 'author_name': 'WorldPop',\n", " 'maintainer_name': 'WorldPop',\n", " 'maintainer_email': 'info@worldpop.org',\n", " 'project': 'Population Counts',\n", " 'category': 'Unconstrained individual countries 2000-2020 ( 1km resolution )',\n", " 'gtype': 'Population',\n", " 'continent': 'Americas',\n", " 'country': 'Guyana',\n", " 'iso3': 'GUY',\n", " 'files': ['https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/GUY/guy_ppp_2020_1km_ASCII_XYZ.zip',\n", " 'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/GUY/guy_ppp_2020_1km_Aggregated.tif'],\n", " 'url_img': 'https://hub.worldpop.org/tabs/gdata/img/31624/guy_ppp_1km_2020_Image.png',\n", " 'organisation': 'WorldPop, University of Southampton, UK, hub.worldpop.org',\n", " 'license': 'https://hub.worldpop.org/data/licence.txt',\n", " 'url_summary': 'https://hub.worldpop.org/geodata/summary?id=31624'}]}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = WP.get_data_by_country(alias='pop', level='wpic1km', ISO3_code='GUY')\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The available file is a geotiff so we need to download it. First we can download a jpg thumbnail of the image we are trying to fetch:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(data['data'][0]['url_img'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import rioxarray as rx\n", "import wget" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This geoTiff for Guyana has a resolution of 1 km per pixel" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100% [..........................................................................] 1214922 / 1214922" ] }, { "data": { "text/plain": [ "'guy_pop.tif'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wget.download(data['data'][0]['files'][0], out=\"guy_pop.tif\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (band: 1, y: 883, x: 588)>\n",
       "[519204 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * band         (band) int64 1\n",
       "  * x            (x) float64 -61.38 -61.37 -61.36 -61.36 ... -56.51 -56.5 -56.49\n",
       "  * y            (y) float64 8.537 8.529 8.52 8.512 ... 1.212 1.204 1.195 1.187\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    STATISTICS_MAXIMUM:  4141.1645507812\n",
       "    STATISTICS_MEAN:     3.0038072312959\n",
       "    STATISTICS_MINIMUM:  0\n",
       "    STATISTICS_STDDEV:   51.022568488192\n",
       "    _FillValue:          -99999.0\n",
       "    scale_factor:        1.0\n",
       "    add_offset:          0.0
" ], "text/plain": [ "\n", "[519204 values with dtype=float32]\n", "Coordinates:\n", " * band (band) int64 1\n", " * x (x) float64 -61.38 -61.37 -61.36 -61.36 ... -56.51 -56.5 -56.49\n", " * y (y) float64 8.537 8.529 8.52 8.512 ... 1.212 1.204 1.195 1.187\n", " spatial_ref int64 0\n", "Attributes:\n", " STATISTICS_MAXIMUM: 4141.1645507812\n", " STATISTICS_MEAN: 3.0038072312959\n", " STATISTICS_MINIMUM: 0\n", " STATISTICS_STDDEV: 51.022568488192\n", " _FillValue: -99999.0\n", " scale_factor: 1.0\n", " add_offset: 0.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tif = rx.open_rasterio('guy_pop.tif')\n", "tif" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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oG78urqqrFv5+ZWvtyl0ee3mS5y/8/SlV9YQkVyX5ztbah5LcPcnrFx5zbbfsk93P25en+/N9SdJau6mqPpLkrkluONqvJCRxANpugJ2oMAGQ5IbW2qX7Paiqbpvka5M8tVv07CRPT9K6P38kybck2anE1PZYnn3uO5LRR1MA1oMDKgDs4R8k+b3W2geSpLX2gdba+dbahSQ/leSy7nHXJrnHwnqXJLmuW37JDstvsU5VXZTk05PceJzBCkkALI2gBMAuHpeFVruqutvCfV+X5C3dzy9LcnlV3a6q7pXNCRp+p7V2fZKPVtVDa/OEpickeenCOk/sfn5sktcc53ykRLsdAEtmCnGA5ajK2k8BniRVdcckfz/Jty4s/qGqelA22+Ku2bqvtfbWqnpRkrcluSnJ6dba+W6dJ+dTU4C/orslyU8n+blukocbs3nu07EISQAAQG9aa3+VzYkUFpc9fo/HPyPJM3ZYflWSB+6w/GNJvv74I/0U7XbsyxFh4LC03QGwzlSSAOiFtjuA46sJtNutI5UkAHqjogTAOhKSAAAAFmi3A6BXW9UkrXcAh1SVOqGmMQTvOvvSLgMsg20JAOtCSAJgZQQlANaBdjv2ZIcGWDaz3gEcTGUaF5NdRypJAKycAzAAjJmQBAAAsEBIAmAQqkkA+6ikNmrUt6kSktiT8waAPglKAIyRiRsAGNRiUHJgBoAxEJIAAGCUKhsuJjsI7zp70goDrJJtDgBjICQBMCqCEgBDE5LYk/MDAACYGyGJfQlKwKqpJgFkcwrwEzXq21QJSQCMkqAEwFCEJA7k5KnTKkrAyglKAAzBFOAAjNq5s2ccpAFmqbp2O1ZPJQmA0Tt39oyqEgArIyRxKNrugCEJSgCsgnY7ANaK9jtgTjZOqGkMwbvOkdhBAYakogRAn4QkAACABdrtAABgjGraF2wdM5UkAACABSpJHIrzAICxMIEDAH0RkgAAYIQqycaGdrshaLcDAABYICRxYFrtAACYA+127Es4AgBgToQkAAAYo0rqhMavIXjXAVhLZrYDoC9CEgAAwALtduzr5KnTzksCABjAxglTgA9BJYkD0dYCAMBcCEkcmKAEAMAcaLfjULaCkvY7YEgO2gCzUJXSbjcIIYnB7LWTI4QBADAUIYkjWQw4Bw00jvwCALAOhCR6d5RwtNs6KkwAwFyUi8kOxrvOWjl56rSKFOCACQC9EpI4tr1CS1+BRlACAKAv2u1YChecBQBYPheTHYZKEkuzvbqj2gP0yYEZAPoiJLFUqzxnSAgDAKAP2u3oxSoCjKPIAMCkVVIb2u2GoJIEAACwQEgCAABYICQBsJaclwhAX5yTxNra2kFybhLM09b/fWEJmKpKZeOEmsYQvOsAAAALhCQAAIAF2u1Ya1rtAIDJqqROmAJ8CCpJAIySc40AGIqQxFqzEwXTpVIMwFC02wEAwBhVUma3G4R3HQAAYEFvlaSqul+SFy4s+oIk/6G19qN9vSbzox0H5m2366VpxQXgOHoLSa21dyZ5UJJU1Ykk70/ykr5ej/kRkIDdnDt7RlACJqBSGxq/hrCqd/1hSd7dWnvvil4PgAk7eer0viHIgRQAjmpVEzdcnuT5O91RVVckuSJJcps7rWg4rDs7P8CWvbYHW/epKgFwGL2HpKq6bZKvTfLUne5vrV2Z5Mok2bjjxa3v8QCw3gQeYC6qkg2z2w1iFe/6P0jye621D6zgtQCYkYNUlYUqAA5rFe12j8surXYAcBiLgWe/gCQcAXBUvYakqrpjkr+f5Fv7fB0ApusoYWe3qcGP85wAzEevIam19ldJ7trnawAwbTtN571XFWm/gLR4n7AEjFulnJM0iFXNbgeHstuFIc1qB/N02FBz0G2F6ykBsBMhiVHYb4dGOAK27FdFsr0A4LiEJAZnhwY4qJ22F4vB6DDbExUkYPQq2u0GIiQxGOEIOI6tcGQacACWTUhiEAIScBwHDT3CEQBHISSxUsIRsCym9wamr1Ib2u2GICSxMgISsJudZrDcaxKGZQWk3WbSBGDehCR6IxQBB7VTWOlzEoY9w1e7kNSnjtwKTgDzIyTRCwEJOI6DbkP2CjBH3g7VLVtbVJuAwVRSJ04MPYpZEpIAWDu9hKMDcgFagOkTkjg2VSNglfYNSNva5fqwtd0TlgCmSUjiwIQhYGi7hZJbbJ96Dkg7va6wBPShUi4mOxDvOgciIAFjNYbt0xjGAMDyqCSxJ1/8wJiNaRu1ffrynZbvRiUKYFyEpBEZ04URx7TjAbDTdnDM26nFyR3q/Cc2F7Z26wdWJbXxqXOpEtOPA4yAkDQSe33Zr7rnfcw7HsD8bN/2rcU2ql3IR3/rx9I2Lkou3PSpANTdl2QzDNXG5mO2/r7tMed++7/d+r4ITzAblWxsODtmCELSSBz2wonJ7q0dR7UWOx7ArBxl2zgKtZFWG5thZzEgLdyfqs3H7LL+jsGqYxpygH4JSWvkINWmxBFGYBrWfVt28tTpfPQ3n5lcOH/LStDNAegAR4e3h6Ut7YKgBNAjIWlEtr7sjnvU1JS0wBSsZQVpm7ZxUWorIC2GnTrR/blHUNq6b6fHdBUq23uYPlOAD8O7PkLL+rI7d/bMgXcyprAzAjAWN7cJducctY2L0k7c9lO3btmxbQtLACyHkDQD+4UlX64Ay7XvdrWbtGGlrwnAgWm3G4lVfLn5AgXo0Q5TePdmp4kdtqYSj/Y7mIwq7XYD8a6PgPACMAFddejmgLLThAs9vN5OVanDtFsDcGtCEgAs0c3hZBUVpX0ISgBHM/wWfIT2+1I5yBG6gx7F8wUGQJ98z8D6qiS1sTHq24F+j6prquoPq+pNVXVVt+wzq+pVVfWu7s+7LDz+qVV1dVW9s6oeubD8wd3zXF1VP15V1S2/XVW9sFv+hqq653Hfe+ckbbP1ZbL9S2Wn/m4hCIAd3WK67+GPR7qmEjACX9lau2Hh79+V5H+31n6gqr6r+/u/q6r7J7k8yQOS/I0kr66q+7bWzid5dpIrkrw+ycuTPCrJK5I8KcmHWmv3rqrLk/xgkm84zmCH33IDMHsnT522E98zB+2AkXl0kud1Pz8vyWMWlr+gtfbx1tofJ7k6yWVVdbckJ1trr2uttSQ/u22dred6cZKHbVWZjkolKSpCAEOYdCgaQfVoJwfpkgBGZD1mt7t4q4Wuc2Vr7cptj2lJfq2qWpKf7O7/nNba9UnSWru+qj67e+zds1kp2nJtt+yT3c/bl2+t877uuW6qqo8kuWuSxcrVoQhJADBT2vCAJbihtXbpPo/50tbadV0QelVVvWOPx+5UAWp7LN9rnSObdUhSHQIYxvYdc9vj4QhKQN9aa9d1f/5ZVb0kyWVJPlBVd+uqSHdL8mfdw69Nco+F1S9Jcl23/JIdli+uc21VXZTk05PceJwxj75+B8C0LO6QC0fj4N8BRqqSOrEx6tu+v0LVnarqzls/J3lEkrckeVmSJ3YPe2KSl3Y/vyzJ5d2MdfdKcp8kv9O15n20qh7anW/0hG3rbD3XY5O8pjtv6chmW0nyhQAAn6KiBPTkc5K8pJtH4aIkv9Ba+9WqemOSF1XVk5L8SZKvT5LW2lur6kVJ3pbkpiSnu5ntkuTJSX4myR2yOavdK7rlP53k56rq6mxWkC4/7qBnG5IAGNZul1xgOFv/FsISsCyttfck+aIdln8wycN2WecZSZ6xw/Krkjxwh+UfSxeylmW27Xa+AABgZ4IrMHezrST5AgCA3akqwThsjH8K8Ema5bsuIAGMgx3w8fOdCczRLEMSAMNYDEUC0voQlIC5mV27nQ09AADroKpSG2oaQ/CuA7AyZrRbX/7NgDmZRSXJhh1gXLTaraft36f+HYGpmnxIEpAAxss2er25AC30r8xuN4jJv+s23gAAwGFMPiQBME6qSNPg3xGYolmEJNUkAOiPoAQ9qUqd2Bj1baqm+5stsPEGGB8HsKbl3Nkzvm+ByZhFSEo2v4y3blt/BwCWS1gCpmDys9sltw5EAhIA9GsrKPnOheNxMdlhzPpdt+EGWD1Vhnnx7w2so1mHJACG5WDVPAhKwLqZdUiy0QYYlu3wfPi3BtbJLM5J2omNNcAwVI/m69zZM/794RCqKhsnTgw9jFmabSXJRhpgWA5WzZPZ74B1MNuQlMR04AAwEGEJGLPZttttEZAAVsc2l+204MHe6sSsaxqD8a4DAINSUQLGRkgCYCVUC9iLoASMyezb7QBYja2d4JOnTtshZkda72Cb0m43FO86ADAaJnQAxkAlKUr8AH3aqTKgmgTAmAlJAPTm5KnTOffb/y1JUu1C2sZF2qk4EK13kCSV2tD4NQTvOgC9uLlaVBubt/OfvPk+VSQOwucEGIqQBEAvtu/gttvcQWUAgLUw+3Y7R6kAjmcx+Oy3TdVCxWH5zDBnZXa7wcz+XbfhBTierdnIHHSiLz5bwKrNPiTZ8AIAAItm324HwGo5OAVwQFXa7QYy63fdFzXA6mlz5igO8p292PrpOx44DpUkAFbKzivLtttnanG5cA4cxqwrSTaYALDeDhq6VZiAw1BJAgDWwrKmA1dhYp3UxqxrGoOZ/btu4wgA62PZ1SDVJWAnsw9JAMD6WXawEZSARdrtcrirxQMA07Ssdj5YmqrUxomhRzFLQhIAsH7ahV6e1vlKQKLd7lZOnjp98w0AGK++uz90l8B8qSTtwoYRAEasNnLy1OnN7+t2IanlH/et85+41f6Ag6isnHa7Qagk7UBAAoA10UPbXV24KXXhph2Dl30EmAeVJABgerbC0xEqTG2fdbaCkqoSTJdKEgAwPctovztgWIL+VLKxMe7bRE33NwMAJm3fkNLDeUo7jUFYgukRkgCA9bbsMFQbh35OQQmmRUjagR5jgNWxzeVYVlAtOihBiaWrpE6cGPVtqsazZQFgluxYMiU+zzANQhIAg1JJYmqcpwTrb9YhaWsjtn1DZsMGAByX/QlYX7O9TpJgBDAOtr9M2bmzZ1RLOYZKNqZ73s+YzbqSBADQNwcCYP3MNiQ5qgMwDrbHzIGgBOtFux0Ag7I9Zi62PusODHBgFe12A5llJckXMgAAsJvJhyQTNAAAY2KKcBi/SYekrQ2QDREAMDb2T9hPpVIbG6O+TdWkzknaa2NjQwQAABzEdOMfAMDIOYgL4zSpShIAAEyG2e0GM4lKkhMgAYB1ZR8GxmftQ5INCwAAsExr3W4nIAEAMF2l3W4ga19JAgAAWKa1DUmqSAAAQB/WMiQJSADAlNi3gXFZu3OSbEQAAJiL2ljLmsba6/Vdr6rPqKoXV9U7qurtVfX3jvN8AhIAANC3vitJP5bkV1trj62q2ya5Y8+vBwAAcCy9haSqOpnky5N8U5K01j6R5BN9vR4AwDo7d/ZMTp46PfQwGJMyBfhQ+qwkfUGSP0/y3Kr6oiS/m+Q7Wmt/ufigqroiyRVJktvc6VZPosUOAABYpT7PSbooyd9N8uzW2hcn+csk37X9Qa21K1trl7bWLq2Lbt/jcAAAxksVCcajz0rStUmuba29ofv7i7NDSNqJ6hEAAGi3G0pvlaTW2p8meV9V3a9b9LAkb9tvPQEJAJgbVSQYl75nt/uXSX6+m9nuPUm+ea8Hf/Hf+ryehwMAALC3XkNSa+1NSS7t8zUAAGCSKqkT2u2G4BK+AAAAC0YVkn7/HX+iJxcAABjUqELSFkEJAAAq2dgY922iRvubCUoAAMAQRhuSEkEJAABYvb6nAD+2k6dOu3YSAADzU3Ex2YGMupIEAACwamsRkk6eOq31DgAAWIm1CElbBCUAAKBvaxWSEkEJAJge51+zs0ptnBj1barWLiQlghIAANCftQxJiaAEAAD0Y21DUiIoAQDTYJ+GXW1sjPs2UWv/m9moAADrzL4MjM/ah6TEFOEAAMDyTCIkbRGUAIB1Yt+FPdX6z25XVfeoqv9TVW+vqrdW1Xd0y7+vqt5fVW/qbl+9sM5Tq+rqqnpnVT1yYfmDq+oPu/t+vKqqW367qnpht/wNVXXP4771kwpJiY0NAACMyE1JvrO19reTPDTJ6aq6f3ffM1trD+puL0+S7r7LkzwgyaOS/ERVbaWxZye5Isl9utujuuVPSvKh1tq9kzwzyQ8ed9CTC0mJoAQAjJ/9FeagtXZ9a+33up8/muTtSe6+xyqPTvKC1trHW2t/nOTqJJdV1d2SnGytva611pL8bJLHLKzzvO7nFyd52FaV6agmGZISGx4AANZdJRsnxn1LLq6qqxZuV+z622y2wX1xkjd0i55SVW+uqudU1V26ZXdP8r6F1a7tlt29+3n78lus01q7KclHktz1OO/8ZENSIigBAEDPbmitXbpwu3KnB1XVpyX5xST/qrV2Lputc38zyYOSXJ/kR7YeusPqbY/le61zZJMOSYmgBDA2tssA81JVt8lmQPr51tovJUlr7QOttfOttQtJfirJZd3Dr01yj4XVL0lyXbf8kh2W32KdqrooyacnufE4Y558SEp8IQOMybmzZ4YeAgzKpUs4sMrwF4s95sVku3ODfjrJ21tr/3Vh+d0WHvZ1Sd7S/fyyJJd3M9bdK5sTNPxOa+36JB+tqod2z/mEJC9dWOeJ3c+PTfKa7rylI7voOCuvk62NkS9nAABYmS9N8vgkf1hVb+qWfXeSx1XVg7LZFndNkm9NktbaW6vqRUnels2Z8U631s536z05yc8kuUOSV3S3ZDOE/VxVXZ3NCtLlxx30bELSlpOnTgtKAACwAq2138rO5wy9fI91npHkGTssvyrJA3dY/rEkX3+MYd7KLNrtAAAADmp2laRE6x0AMJyt/Q/nJbGfSqVOnNj/gSzdrCtJNk4AwFAcrIXxmnVISgQlAADglmbZbredyRwAABidSrKh3W4Is68kbXHNAgAAIBGSbkVQAgCAedNutwPtdwAADK+02w1EJWkXKkoAADBPQtIeBCUAAJgf7XYAADBStaGmMQTv+j5UkwAAYF6EpAMQlACAZbN/AeOl3e6AFjdkZr4DAKB3ZXa7oagkHYEjPwDAUbmAPYyfkHRENm4AADBN2u0AAFbEQVYOrdQ0huBdPwYbOgAAmB4h6ZgEJQDgIOwzwPoQkpbARg8A2It9BVgvzklakq2Nn+nBAQBYjnJO0kC860vmSBEAsMi+AawfIakHNoYAQGKfANaVkNQTG0UAmDf7AixDq41R36bKOUkAAEskHMH6E5J6ZDIHAJgP4QimY7o1shGx0QQA4NAqm7Pbjfk2UdP9zUbm5KnTwhIATJTveJgWIWnFbEQBAGDchKQBCEoAAOyvkhr5baKEpIEISgAAME5C0oAEJQAAGB8haWCCEgAAu9rYGPdtoqb7mwEAAByBkDQCqkkAADAeQtJICEoAADAOFw09AD5lKyidO3tm4JEAAAflQCd9aUlaqWkMwbs+QidPnbbBBYA14PsapklIGjEbXgAYL9/TMF3a7Ubu5KnT2u8AYCQEI1aqKtFuNwjv+hqwQQaA4fk+hvkQktaEDTMADMf3MMyLdjsAABgr7XaD8K6vEbPeAcDq+e6F+RGS1pCNNQAA9EdIWlOCEgD0z/ctw+pmtxvzbaKm+5vNgA03AAAsn5C05gQlAOjPTtcqPHf2jGsYwsSZ3W4CXHAWAPrjO5YhtQm3tI2Zd30iVJQAAGA5hKQJMUU4AAAcn3a7CdJ+d3A7hUrvHQDAvAlJEyUo7U61DYA+LX7/+s7h2JyTNAjv+oTZMN/SQdsR93vc1v3eXwB2svgd4YAlrCeVJCZtmUFGKAIAmAchaeLm2Ha3rDAjFAGwl3Nnzxzou+Kgj4Nbqdq8sXJC0swdZqM9t7AFAPsRgGCahKQZ2KmadJQN+uI6qwxMh+nrXvUX1RwrdQDc0kG+B4QpWC9C0kws7swvYyN93OBylPX2ep4hv3gEJQAOQlDiSMxuNwghaUb62DDvFpb2e63t9x+10uXLBoCh7fZd5AAarC/RlKVYnBb7uK1862jdxw/A0ex3yYjtBCdYDypJjIagAcA6OMz3lZZsjqtptxuEdx2WRMgDYCdb3w8uRA7rQ0gCAOiZcATrRUiCJfIlCAAsTyUbG+O+TZRzkmDJhrqeFAAAyzHd+AcjoLIEMC226zAPQhL0zBcqAHAklc2LyY75NlHT/c1gRAQlgPVnWw7zISTBivhyBQBYD0ISrJBrZACsJ9tumBez28EAdvqyNRMeAHBLNenzfsbMuw4j4SglwDjZPsP8CEkwEipJAONk+wzzIyQBAOxDUGIwQ0/xPdMpwJ2TBACwj62Wu8WwpA0PpktIAgDYxfYgJBjBPAhJMALaOAD21/fMoAIQY9Qm3NI2Zr2GpKq6JslHk5xPclNr7dI+Xw8AGJ/9wsd+QWev9RfvO2pgEo6A7VZRSfrK1toNK3gdAGBEDho+lhVSdnue3cKTcATsRrsdDEyrHTBFYwogYxoLHEq5mOxQ+g5JLcmvVVVL8pOttSu3P6CqrkhyRZLkNnfqeTgAQF+EEWAq+g5JX9pau66qPjvJq6rqHa2131h8QBecrkySjTte3HoeDwDQAwEJmJJeQ1Jr7bruzz+rqpckuSzJb+y9FgCwTgQk6FHV0COYpd6aHKvqTlV1562fkzwiyVv6ej0AYPUEJGCK+qwkfU6Sl9Rm+r0oyS+01n61x9cDAAA4tt5CUmvtPUm+qK/nBwD6s4zrDwGsK1OAAwC3sL2Fbvvfz509o80OVsIU4EMRkmBAjs4CY7Lqi78CjJVoCgAIPgALVJIAYOYEJBivpt1uEEISDECbHTAWAhLArYmmADBTAhLAzoQkAJghAQnWRG2M+7bf8KseVVXvrKqrq+q7VvCOLYWQBAAALF1VnUhyJsk/SHL/JI+rqvsPO6qD2TckVdVTquouqxgMANA/VSRgRS5LcnVr7T2ttU8keUGSRw88pgM5yMQNn5vkjVX1e0mek+SVrbXW77AAgGUTjmC9tKq0qqGHsZ+Lq+qqhb9f2Vq7svv57knet3DftUkesrKRHcO+Iam19u+r6nuTPCLJNyd5VlW9KMlPt9be3fcAYWrMbAesmnAE9OiG1tqlu9y3U8Jbi2LLgc5J6ipHf9rdbkpylyQvrqof6nFsAADA+ro2yT0W/n5JkusGGsuh7FtJqqpvT/LEJDck+R9J/t/W2ieraiPJu5L8236HCNOhigQAHFhL1vwklzcmuU9V3SvJ+5NcnuQbhx3SwRzknKSLk/yj1tp7Fxe21i5U1df0MywAYBm02gFDaa3dVFVPSfLKJCeSPKe19taBh3UgBzkn6T/scd/blzscmC5VJABgblprL0/y8qHHcVgHqSQBAAAr13Jhzfvt1pWLyQLAhKliAxyekAQAALBASIIVcfI0AMB6cE4SAEzcTi13DtzAenBG0jBUkgBghpyrBLA7IQkAZkpQAtiZdjtYETsjAMBhtCQX9NsNQiUJAABggZAEAACwQEiCFTGTFDBGWoFh3Fpro75NlZAEK2JHBABgPQhJsAICEjBmtlEAt2R2O+iZnQ8A4CjMbjcclSTokYAErAvbK4BPEZKgJ3Y4AADWk3Y7WDLhCABYFt12w1BJAgAAWCAkAQAALBCSAAAAFjgnCQAAxqiZAnwoKkmwRCZtANbVyVOnhx4CwGgISQAwcwISwC1pt4NDUi0CAFalNf12Q1BJAoAZU0UCuDUhCQBmSkAC2Jl2OzgALXYAwKq1JBeGHsRMqSQBAAAsEJIAYIa02gHsTrsdAACMlMnthqGSBEty8tRpR2YBACZAJQkOYKfws9tkDouPNeEDMEYO6ADsTUiCI9pvJ+OoAenkqdO3WHf73wGA+big3W4QQhKsyEGqUVuP2f5YQQkAYHWEJOjJQdpZth5znAAkQAEHpc0O4GBM3AAjcJBJH3a6f7HyZOcHAGA5VJJgDR1mIgmARBUJ1lFrSTMH+CBUkmCN7FUx2msHyM4RzJttAMDhqCTBhGzfETJLHgDA4QlJMGFmyQNUkWC9XRh6ADOl3Q5mxg4TzIf/7wBHIyTBDNlxgunz/xzg6LTbwUztdf4SADAOJrcbhkoSkMRRZ5gS/58BjkdIAm5mxwrWmwtLAyyHdjvgFrZ2sLTfAcCwWpIL+u0GISQBO9rpaLTgBADMgXY7AACABSpJAAAwUprthqGSBByIVjsAYC6EJOBAzJgFAMyFdjvgQFSSAGD1Lui3G4RKErAvAQnGT7UXYHlUkgBgTQlGAP1QSQL2pIoEAMyNShKwI+EIAIbXnJM0CCEJuAXhCACYO+12AAAAC1SSAABghFpaLkS/3RBUkoBbOHnqtBmzYA34fwrQHyEJuBXnJcG4CUgA/dJuB9yCgATjJiDBjDSz2w1FJQkAAGCBShIArAEVJIDVEZIAYOQEJJivC9rtBqHdDgAAYIGQBAAjpooEsHra7QAAYIRazG43FJUk4BYctYZxcGFngOEIScCt2DEDAOZMSAKAkXGgAmBYzkkCdrS4k3bu7JkBRwIA83UhTkoagpAE7Guvo9oCFCyXKhLA8LTbAcfi5HIAYGpUkgAAYKRMAT4MlSQAAIAFQhJwbM5LguXx/wlgeEISAIyMoAQkSUtyobVR36ZKSAKOxc4cADA1QhJwJOfOnhGQoEf+fwEMx+x2AAAwRi05f2HoQcyTkAQcyeK1kRzxBgCmRLsdcGwuKAsATImQBCyNoATLpUoL82Z2u+EISQAAAAuEJGCpVJMAgHXX+8QNVXUiyVVJ3t9a+5q+Xw8YlvYgAFiWlvMTbmkbs1VUkr4jydtX8DrACJjEAQBYd72GpKq6JMk/TPI/+nwdAACAZem73e5Hk/zbJHfu+XWAkXEdJQBgXfUWkqrqa5L8WWvtd6vqK/Z43BVJrkiS3OZOfQ0HAADWytYU4Kxen+12X5rka6vqmiQvSPJVVfU/tz+otXZla+3S1tqlddHtexwOAADA/nqrJLXWnprkqUnSVZL+TWvtn/X1esB47TWRg1Y82NvW/xETogCsjuskAYOy4wcAu2jJ+Qvjvh1HVf1/VfWOqnpzVb2kqj6jW37PqvrrqnpTd/vvC+s8uKr+sKqurqofr6rqlt+uql7YLX9DVd1zYZ0nVtW7utsTDzK2lYSk1tprXSMJAI7u3NkzKq/A1LwqyQNba1+Y5I/SdaF13t1ae1B3+7aF5c/O5nwG9+luj+qWPynJh1pr907yzCQ/mCRV9ZlJnpbkIUkuS/K0qrrLfgNTSQKANSIoAVPRWvu11tpN3V9fn+SSvR5fVXdLcrK19rrWWkvys0ke09396CTP635+cZKHdVWmRyZ5VWvtxtbah7IZzB6VfQhJwKDs8MHh+X8D87A1u92Yb0kurqqrFm5XHPHX/ZYkr1j4+72q6ver6ter6su6ZXdPcu3CY67tlm3d974k6YLXR5LcdXH5Duvsqu/rJAHcih08OL5zZ884pw8Ygxtaa5fudmdVvTrJ5+5w1/e01l7aPeZ7ktyU5Oe7+65P8nmttQ9W1YOT/HJVPSBJ7fA8W3Ok73bfXuvsSkgCVkpAAoD5aK09fK/7u4kUvibJw7oWurTWPp7k493Pv1tV705y32xWgRZb8i5Jcl3387VJ7pHk2qq6KMmnJ7mxW/4V29Z57X7j1m4HrIyABACHc761Ud+Oo6oeleTfJfna1tpfLSz/rKo60f38BdmcoOE9rbXrk3y0qh7anW/0hCQv7VZ7WZKtmesem+Q1Xeh6ZZJHVNVdugkbHtEt25NKEtA74QgA2MGzktwuyau6mbxf381k9+VJvr+qbkpyPsm3tdZu7NZ5cpKfSXKHbJ7DtHUe008n+bmqujqbFaTLk6S1dmNVPT3JG7vHff/Cc+1KSAKANXXQAxDOXQLGqJuue6flv5jkF3e576okD9xh+ceSfP0u6zwnyXMOMzbtdkCvVJFgeP4fwnranN1u3LepEpIAAAAWaLcDgBlYrCZpvwPYm0oSAADAApUkAJgZF6KFNdGS81M+8WfEVJKAXtkRAwDWjZAEADNkxjuA3QlJQK/siMF4+f8J49bScqGN+zZVzkkCemHnCwBYVypJwFKdO3tGQII14v8rwK2pJAHHZicLAPpxfrodbaMmJAGHJhTBtJgSHOCWtNsBhyIgAQBTJyQBAA6AwAi1ZPDZ6+Y6u52QBAAAsEBIAgAAWGDiBgAAGKOWnL8w3Za2MVNJAgAAWCAkAYdimmAAYOqEJAAAgAXOSQIOxTTBALAaW1OAs3pCErAvwQjmYev/urZaYO602wF7EpAAgLlRSQJuRTACgHE4r9tuECpJAMAtOFACzJ2QBNyCnSMAYO6028HMCUXATha3DSZygGGY3W44KkkAwJ7OnT3jgAowKypJMDN2dICjMkU4MBdCEkycUAQsm1Y8WJHWcuGCdrshCEkwQYIRsCqqS8AUOScJJsR5A8BQbHuAKVFJAgCAEWpxMdmhqCQBAEuhmgRMhZAEE2HnBABgObTbAQBLYyIHWC4Xkx2GShIAsHSq28A6E5JgIhy1BcZGUALWlZAEE3Ly1GlhCRgVlyYA1pFzkmCCtoKSHRMAWF+bU4A7J2kIKkkwYapKwFg4aAOsEyEJJk5QAgA4HO12MAMnT512FBcA1k1LLlzQbjcElSSYAQEJAODghCSYOAEJGAOtv8A60W4HAAAjtDm73dCjmCeVJACgV6pIwLoRkmDCtNoBAByedjuYIOEIGAtVJDieCy4mOwiVJAAAgAUqSTBB24/cqiwBAByckAQAACPU0nJeu90gtNvBxKkiAUOyDQLWkZAEAPRKUALWjZAEE2dmKQCAw3FOEgDQu61q0m4HbnarNjnQw6y15PwF5yQNQUgCAFZmp7C0VzvefuFq1RbHOpYxAcun3Q5mwBc5MDbnzp65+XbQxw9tDGMAVkMlCQBYC+fOntm1AnWUg0GLVSoBiDFq0W43FCEJJsyXPjA1u23Xtgeow6x/1G3lQV8TWD9CEgAwCbuFFgeMgMMSkgCAyRCImJJmdrvBCEkwQXYSAACOzux2MDECEgDA8agkwYQISACrY9IGVkG73TBUkgAAABYISTARqkgAAMuh3Q4mQEACgOlpadrtBqKSBABwBA5QwXQJSbDmfEkDACyXdjsAABgjF5MdjEoSrDFVJACA5ROSYI25RgcAwPIJSQAAAAuckwRrRosdwDio5tO3FuckDUUlCdaIgAQA0D8hCQAAYIF2OwAAGKFmCvDBqCTBGtH/DgDQPyEJAABggXY7AIAj2JpMR5WfPmm3G4ZKEqwRs9sBjIuABNMkJMEa8WUMANA/7XYAADBCLU273UBUkgAAjmh7G7S2aJgGlSRYE754AcZpcfusLRqmQUiCkROOANaDgMSytZbcpN1uENrtYMQEJID1YZsN06GSBACwJFrvYBqEJBghRyMB1p/ABOtLSIKREZAApufc2TOCEkdiCvBhOCcJAABggZAEAACwQEiCkdGOAQAkm1OAn7/QRn07jqr6vqp6f1W9qbt99cJ9T62qq6vqnVX1yIXlD66qP+zu+/Gqqm757arqhd3yN1TVPRfWeWJVvau7PfEgYxOSAACAoTyztfag7vbyJKmq+ye5PMkDkjwqyU9U1Ynu8c9OckWS+3S3R3XLn5TkQ621eyd5ZpIf7J7rM5M8LclDklyW5GlVdZf9BtXbxA1Vdfskv5Hkdt3rvLi19rS+Xg+mwsQNANOhOwCO5NFJXtBa+3iSP66qq5NcVlXXJDnZWntdklTVzyZ5TJJXdOt8X7f+i5M8q6syPTLJq1prN3brvCqbwer5ew2gz9ntPp7kq1prf1FVt0nyW1X1itba63t8TVhrAhIAsOh8G/3sdhdX1VULf7+ytXblIdZ/SlU9IclVSb6ztfahJHdPspgZru2WfbL7efvydH++L0laazdV1UeS3HVx+Q7r7Kq3kNRaa0n+ovvrbbrb6P+VAQCWZevgl4oSE3ZDa+3S3e6sqlcn+dwd7vqebLbOPT2bGeHpSX4kybckqR0e3/ZYniOus6ter5PU9Q7+bpJ7JznTWnvDDo+5Ipt9hclt7tTncGD0tr5EVZQApkNAYs5aaw8/yOOq6qeS/Er312uT3GPh7kuSXNctv2SH5YvrXFtVFyX59CQ3dsu/Yts6r91vPL1O3NBaO99ae1A3mMuq6oE7PObK1tqlrbVL66Lb9zkcWAsCEgCQJC3Dz17X8+x2d1v469cleUv388uSXN7NWHevbE7Q8DutteuTfLSqHtqdb/SEJC9dWGdr5rrHJnlN19n2yiSPqKq7dBM2PKJbtqdeK0lbWmsfrqrXZvMkqbfs83AAAGD6fqiqHpTN9rdrknxrkrTW3lpVL0rytiQ3JTndWjvfrfPkJD+T5A7ZnLDhFd3yn07yc90kDzdmc3a8tNZurKqnJ3lj97jv35rEYS99zm73WUk+2QWkOyR5eLqp+ICdqSIBAHPRWnv8Hvc9I8kzdlh+VZKdutM+luTrd3mu5yR5zmHG1mcl6W5Jntedl7SR5EWttV/ZZx0AACCfupgsq9fn7HZvTvLFfT0/TNHJU6dVkwAmzox3MH69TtwAHI6ABDA9i9t223lYDyuZuAHYny9OgOla3MarIMH4CUkwAgISwDwISByWc5KGISQBAPRIMIL145wkAACABSpJMAJmtQMAtmtJzl+4MPQwZkklCUZAQAKYLtt4WD9CEgBAzwQlWC/a7QAAYIxaM7vdQFSSAABWQDUJ1oeQBACwAqYCh/Wh3Q4AAEZoc3Y77XZDUEmCgWm/AJgH23tYHypJAAArsj0oacGDcRKSAAAGsFNA2gpRwhNJ0lpyk3a7QQhJMLCTp05rwQCYod22/QISDM85SQAAIyEgwTioJAEAwAiZ3W44KkkwMK12AADjopIEA3NOEgBbnKcE46CSBAAwYgISrJ5KEgAAjFFzTtJQVJIAAAAWCEkwMOcjAbAX3xOwetrtAABG7tzZM85NmqGWpt1uICpJAABrQEUJVkdIAgBYE4ISrIZ2OxiY6yQBALvRbjcMlSQAgDXiwBr0T0gCAABYoN0OAABGqLmY7GBUkmAETp46bWpXAA7s3Nkz2u6gR0ISAADAAu12AABrbquqpCthepp2u0EISQAAa0rLHfRDux2MiCOAAByV7xBYHiEJAABggXY7AAAYodaSC85JGoRKEgDABDg/CZZHSAIAAFggJMHIOPEWANjU0tq4b1MlJMEInTx1+uYbAByUljtYDiEJAGBCBCU4PrPbAQBMzGJQ0pWw3prZ7QahkgQAALBASIKRcwQQAGC1tNsBAMAYuZjsYFSSYA2oJgEArI6QBAAAsEC7HQDAhG2fEvzkqdM3L9OpMG4tSbsw9CjmSSUJ1oQvMgCWwXWUYH9CEqwRQQmAZTh56rTvFNiDdjtYI47+AXBcwtF6ac3sdkNQSQIAmBEH3GB/QhIAAMACIQnWiBYJAJbh3NkzKkqwB+ckAQDM1LmzZxyAG7OWXLjgnKQhqCQBAAAsEJIAAAAWaLcDAJgprXZj19K02w1CJQkAYKZM3gA7E5JgjfgyAwDon5AEa0RbBADL5gDceLUk7UIb9W2qnJMEADBzi0HJATlQSYK148sLgD6pLIGQBGtPaAJg2c6dPSMsjUFLLrQ26ttUabeDNSQYAbAK586e8Z3DLKkkwQScPHXalxgAS+e7hbkSkmBCtr7MhCYAmIahZ6+b6+x2QhJMzGI4EpQAAA5PSIKJE5QAAA5HSIIZEJQAAA5OSIKZEJQAOCzTgA9v6HOO5npOkinAYUYOEpR8IQKwaOt7wcE25kRIAgBgX4sH0QQmpk5IAgDgUASm1Wit5cKEW9rGzDlJAAAAC4QkAACABdrtAABgpFrTbjcElSQAAIAFQhIAAMAC7XYAADBS7cLQI5gnlSQAAIAFQhJws8XrXgDAQfjuYIq02wEAcCznzp5xUdketBYXkx2IShIAAMACIQkAgGPTdseUCEkAAAALnJMEAAAj1ZyTNAiVJOBmTroF4Di03DEVQhIAAEsjKDEF2u0AAFiqxaCkS+EY2rTb7arqhUnu1/31M5J8uLX2oKq6Z5K3J3lnd9/rW2vf1q3z4CQ/k+QOSV6e5Dtaa62qbpfkZ5M8OMkHk3xDa+2abp0nJvn33XP9p9ba8/Ybm0oSAAC9UVliN621b2itPai19qAkv5jklxbufvfWfVsBqfPsJFckuU93e1S3/ElJPtRau3eSZyb5wSSpqs9M8rQkD0lyWZKnVdVd9hubkAQAAAymqirJP0ny/H0ed7ckJ1trr2uttWxWjh7T3f3oJFsVohcneVj3vI9M8qrW2o2ttQ8leVU+Fax2JSQBN3O0D4A++H45qpYLbdy3JBdX1VULtyuO8It+WZIPtNbetbDsXlX1+1X161X1Zd2yuye5duEx13bLtu57X5K01m5K8pEkd11cvsM6u3JOEgAAvTt39ozzk6bphtbapbvdWVWvTvK5O9z1Pa21l3Y/Py63rCJdn+TzWmsf7M5B+uWqekCS2uF5tk7a2u2+vdbZlZAEJHGUDwBYvtbaw/e6v6ouSvKPsjnhwtY6H0/y8e7n362qdye5bzarQJcsrH5Jkuu6n69Nco8k13bP+elJbuyWf8W2dV6737i12wEAsBIOyB1Oy+bsdmO+LcHDk7yjtXZzG11VfVZVneh+/oJsTtDwntba9Uk+WlUP7c43ekKSrWrUy5I8sfv5sUle05239Mokj6iqu3QTNjyiW7YnIQkAgJURlNjm8tx6woYvT/LmqvqDbE7C8G2ttRu7+56c5H8kuTrJu5O8olv+00nuWlVXJ/l/knxXknTrPT3JG7vb9y8816602wFJNq9j4YsLAFil1to37bDsF7M5JfhOj78qyQN3WP6xJF+/yzrPSfKcw4xLJQm4mRNqAVgFB+UOqM2i3W6UhCTgFgQlAFbh3NkzwhKj1VtIqqp7VNX/qaq3V9Vbq+o7+notYHl8YQGwSr53GKM+z0m6Kcl3ttZ+r6runOR3q+pVrbW39fiawDH4ogIY3n4V/Sluq5f5O02tI+LChFvaxqy3SlJr7frW2u91P380ydtzgKvbAgDM0clTpw+0gz+1ELBsUwyRrN5Kzkmqqnsm+eIkb9jhviuq6qqquqrd9LFVDAcAYFQEn+USlDiu3qcAr6pPy+YUfv+qtXZu+/2ttSuTXJkkG3e8WD0RABilvYLMUXfKhaP+nDt7xvvLkfUakqrqNtkMSD/fWvulPl8LOB5H3QB2dtAWuP22o3bYV2/7v8k6/hu0poYwhN5CUlVVNq98+/bW2n/t63UAAPqyrJ3qZe+cbz2fA1yHs/V+3eGOzx14JIxdn+ckfWmSxyf5qqp6U3f76h5fDziGdTy6BtCnw24Xd3u87Susn94qSa2130pSfT0/sFyORgJsWmao6TsgqShNW2stzRTgg+h94gYAgLkYqmp0kHOigIMTkgAAOuvcGjfVoLT4b3Lc32+d/31ZLSEJmOSXKsBhTWEHeipBab/zuw77O67zv+0F7XaDEJIAgNla553n3exXeenjek8HtdNrL77mQf899gqDU/w3ZfWEJABgNua2A33cGfqWFZr2GsdR/03m9m/JaglJAADsyOx5w2sXzg89hFkSkgCAWVB5OLrjVJi876wjIQkAgEPZb4IIwYh1JyQBAHBou1WXBKQlak273UCEJAAAjk04YkqEJOBWJ+b2NbsRwFDswAOHISQBN9vv4n2LBCcAYKqEJOBITAsLrAtVJNZVi3OShrIx9AAAAPoiIAFHISQBAAAs0G4HHJoWO2AdqCKx9lrSzmu3G4KQBByYcASMnWAELIN2OwAAgAUqSQDAJKgiMT1mtxuKShJwYHZAAIA5EJKAQxGUgDGybQKWSbsdcGgnT502iQMA9K1ptxuKkAQcyeJRW4EJGJIqErBsQhJwbFs7KOfOntl3Z0WgAgDGTkgCluYgR3O16gHLpIrE1Gm3G4aQBKycoATr7aDBxP9zYF0JSQDAgRy2atPnuYsqSECfhCRgEIvnMQHjd9xQsn39/f7v7xawhCPmpLmY7GCEJGBQWu9g3PoKJYd5XsEIWDUhCRicoATDEkIAbklIAkZBUILDO845P4IRwO6EJGA0BCU4usOc8yMgwZpopgAfipAEjIqgBMuxfXIUwQjg4IQkAJgw4Qjg8IQkYHRUkwAgSVouaLcbxMbQAwDYiaPfAMBQVJKA0TrOzF0AAEclJAFrQQseALPTmtntBqLdDlgbWvAAgFUQkoC1IijRp5OnTvuMAaDdDlg/26//Ase1PRj5jAFj0OJiskNRSQJg1vaqHKksAcyTShKwthzt57gOGoC2P277Z26/5/EZBVgvQhIAs3Pc6pDqErASraWd1243BO12wNqzw8phDPF58RkFWC9CEjAJdkI5CJ8TAA5CSAJgNpwbBMBBOCcJmIy9qgR2jhmaiUaAozAF+DCEJGAWTp46beeUUdjps2h2PIBxEZIAYMUOe26Uc6kAVktIAmZDNQmAtdKadruBmLgBmBVH5AGA/agkAbPjBPp5EYwBOCwhCYC1IfAA86Ldbija7YDZOnnqtJ1uAOBWhCRg9gQlAGCRdjsAABihlqRduDD0MGZJJQkgqknrwmQbAKyCkAQAALBAux1Ax8VmARgVF5MdjEoSwAJtd+MnyALQNyEJYBtBCQDmTUgC2MHWNZQEpnFSTQKgT85JAtjHVlCyYw7AqjknaRhCEsABba8qCU3DOnf2jEofAL0QkgCOSGgCgGkSkgCWRGgCYKlaywXtdoMQkgB6slMrmOAEAOMnJAGs0EHOoRGkAGBYQhLAyKhAAZAkLUk7r91uCEISwBrYrQIlPAHA8glJAGtszpNFmP4bgL4ISQATcvLU6VkEJQEJmIXWXEx2IEISAEeyzBbAw1TEBCQA+iYkAUzM0NWk/ULM9rHt9PjdfgcBCYBVEJIAWKmjBB3hCJgn7XZDEZIAJmYVVaRVhBbBCIChbAw9AAAAgDFRSQIAgJHSbjcMlSSAiTl56nSvrWra4ACYOiEJYKKEGQA4Gu12ABO2GJSWMaGD4AXAHAhJADNx1OsnCUYAA2mmAB+KkAQwI1uB5yBhSTgCYK6EJIAZ2issCUcAzF211oYew82q6s+TvHfocSzRxUluGHoQjJrPCHvx+WA/PiPsx2dkZ5/fWvusoQexn6r61Wz+G47ZDa21Rw09iGUbVUiamqq6qrV26dDjYLx8RtiLzwf78RlhPz4jcDSmAAcAAFggJAEAACwQkvp15dADYPR8RtiLzwf78RlhPz4jcATOSQIAAFigkgQAALBASAIAAFggJPWkqv5lVb2zqt5aVT/ULbtrVf2fqvqLqnrW0GNkODt9PrrlT62qq7v7HjnkGBlOVX1fVb2/qt7U3b66W37bqnpuVf1hVf1BVX3FsCNlKHt8Rm5TVc/rPiNvr6qnDj1WVm+Pz8c/XVj2pqq6UFUPGni4MEoXDT2AKaqqr0zy6CRf2Fr7eFV9dnfXx5J8b5IHdjdmaLfPR1XdP8nlSR6Q5G8keXVV3be1dn640TKgZ7bWfnjbsn+eJK21v9N9bl5RVV/SWruw+uExAjt9Rr4+ye26z8gdk7ytqp7fWrtm9cNjYLf6fLTWfj7JzydJVf2dJC9trb1pgLHB6Kkk9ePJSX6gtfbxJGmt/Vn351+21n4rm2GJ+drx85HN4PSC1trHW2t/nOTqJJcNNEbG6f5J/ndy8+fmw0lcJJJFLcmdquqiJHdI8okk54YdEiP1uCTPH3oQMFZCUj/um+TLquoNVfXrVfUlQw+IUdnt83H3JO9beNy13TLm6SlV9eaqek5V3aVb9gdJHl1VF1XVvZI8OMk9hhsiA9vpM/LiJH+Z5Pokf5Lkh1trNw42Qoa00+dj0TdESIJdabc7oqp6dZLP3eGu78nm+3qXJA9N8iVJXlRVX9DMtz4bR/l8JKkdHu8zM1H7fEaeneTp2fz3f3qSH0nyLUmek+RvJ7kqyXuTnE1y0yrGy+od8TNyWZLz2WzZvUuS36yqV7fW3rOSQbMyR/x8bK3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