The map seeks to illustrate the evolution of the reach of rural and urban financial services in Peru. It compiles data from available public sources on a variety of financial service providers, the extent of their outreach, and the location of their services. These financial service providers are then compared to data on rural and urban areas drawn from global geospatial databases and national statistics offices based on population density.
To understand how classification of urban and rural regions might be expanded upon, we sought out data sources that allowed us to move beyond country-specific definitions of what was considered urban or rural. Specifically, we sought to find databases on urban and rural data that were global but were also finely-grained enough to be useful, i.e. they contained information that would allow us to delineate between urban and rural regions at sub-national levels such as districts.
Global datasets that we found suitable for our purposes were the Global Rural-Urban Mapping Project/Gridded Population of the World (GRUMP/GPW) dataset and the Anthropogenic Biomes (AB) dataset. Both contain data for nearly the entire world and are free to access through the Socioeconomic Data and Applications Center (SEDAC) website at Columbia University.
Overall, the two databases combine several useful properties for measuring rural financial services: * They are publicly-available and cover the vast majority of the developing world * They are finely-grained enough to allow focus on very small geographic units * They contain additional information, beyond population density, that is directly connected with the provision of services to and the livelihoods of those living in urban and rural areas.
The financial institutions data was sourced from SBS, based on ‘snapshots’ of the data available at the end of financial quarters or bi-annually. NGO financial service providers are not covered in this data.
All the colors displayed in this map are scaled based on the 20th, 40th, 60th and 80th percentiles of the respective dataset they represent. The size of the circles depicts the most recent value for that variable. The symbol shown in the circle depicts the trend over time, i.e., + for an increasing trend, - for a decreasing trend, and a diamond to indicate starting and end values that are the same.
The population contextual layers are sourced from the country’s statistics bureau (linked to below the colored bar in the legend), the urban extents and population density data from the Global Rural and Urban Mapping Project (GRUMP) dataset, and the Anthropogenic Biomes dataset.
The GRUMP and Anthropogenic Biomes (AB) populations were calculated using the GRUMP population density file for 2000 which was overlaid with maps illustrating urban and rural regions as classified by GRUMP and Anthropogenic Biomes. While the GRUMP dataset was binarily classified as urban or rural, the Anthropogenic Biomes dataset relied upon a calculated percentage of the population in each biome being classified as urban or rural.
We used the percentage of non-urban population to calculate urban and rural percentages show on the map; so in the page above, in a biome called Urban 0.2% of the population is considered rural. For each of the twenty-one biomes a percentage of non-urban population is given. Lesser-developed biomes like deserts or tundra have a small percentage of population considered urban by the AB dataset where as a biome such as dense urban biomes or irrigated farmlands have a higher percentage of the population that is classified as urban. The Anthropogenic Biomes dataset is a global dataset which divides the planet’s surface into six classifications based upon human interaction with the environment: urban, villages, croplands, rangelands, forested, and wildlands. Subcategories within each of these six further delineate the six classifications: * Urban – urban and dense * Villages – rice, irrigated, cropped and pastoral, pastoral, rainfed, and rainfed mosaic * Croplands – residential irrigated, residential rainfed, populated irrigated, populated rainfed, and remote * Rangelands – residential, populated and remote * Forested – populated and remote * Wildlands – barren, sparse and wild
Datasets in the GRUMP/GPW database of particular interest are population density maps for the world, which we used in conjunction with a dataset that delineated the extents of urban regions. The Urban Extents dataset determines the boundaries of urban regions given population counts (number of persons), settlement points, and nighttime lights data gathered via satellite imagery. The definition of an urban region in that dataset is: a region where contiguous lighted cells from the Nighttime Lights dataset occurred or where settlement points with a total population of 5,000 persons existed. The presence of nighttime lights is a strong proxy for the existence of basic services, like electricity, and the surrounding infrastructure. By combining the Urban Extents data with population density maps we were able to ascertain the population inside a given region indicated as urban, and conversely, the population outside of the urban region, the rural population.
The map files representing national, state and district boundaries was sourced from Global Administrative Areas (GADM), an international database of geographic files (shapefiles) covering every country in the world. The data are maintained by contributors working at the International Rice Research Institute and the University of California, Berkeley’s Museum of Vertebrate Biology. The shapefiles are drawn primarily from the Centers for Disease Control and the UN Geographic Information Working Group’s “Second Administrative Level Boundaries” project. The map used is a product based on the GADM database. The boundaries shown on this map do not imply, on the part of MIX, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.
The data seen on this site is available in the following formats: <div class="dl-buttons"> Fusion Table
Visit the MIX Market Peru page for more information on microfinance institutions in Peru.