Accurate, up-to-date maps of and georeferenced data about human population distribution are essential for meeting the United Nations Sustainable Development Goals progress measures, for supporting real-time crisis mapping and response efforts, and for performing many demographic and economic analyses (e.g., Blumenstock 2016; Geldmann, Joppa & Burgess 2014; and Martin, Maris & Simberloff 2016). To aid in these efforts, Esri published the initial version of the World Population Estimate (WPE) image service to its ArcGIS Online portal in December 2014. The WPE is a global dasymetric estimate of the footprint of human settlement, where each populated raster cell in the footprint contains an estimate of the number of people living there. In the summer of 2016, Esri published an additional image service for the world’s population in 2015 based on an improved methodology for estimating the footprint. The improved methodology is presented here. With the 2015 estimate, Esri also released image services of population density, settlement likelihood scores, and confidence scores for settlement likelihood. Figure 1 shows the global extent of the footprint as a map of population density.
Esri’s image services are a type of web service, making one or more raster datasets available to ArcGIS software users via the ArcGIS Online portal. The services can be accessed by many users simultaneously. These services provide access to the data such that they do not need to be copied and can be used directly as input to ArcGIS geoprocessing tools and models or ArcGIS web application program interfaces (APIs). Use of these services is free for all registered ArcGIS software users.
The 2013 estimate is provided at a raster resolution (cell size) of 250-meters globally, and the 2015 estimate is provided at 162-meter resolution. One of the improvements to the 2015 estimate was moving the production from the Web Mercator Auxiliary Sphere coordinate system to the World Geodetic System WGS 1984 geographic coordinate system. This also means the raster resolution is expressed as the width of a cell at the equator. Figure 2 shows an area in central Spain, with Madrid at the center, to illustrate the geographic precision or level of detail 162-meter resolution raster data provides.
Esri produced the WPE as a response to user’s needs to perform economic and demographic analyses with a high level of geographic precision. The WPE is used by the Data Enrichment tool in Esri ArcGIS Online software and by Esri Business Analyst software. Other potential uses for the WPE include estimating populations affected by natural disasters, disease outbreak, and other humanitarian emergencies and estimating the impact of human activity on the landscape.
The additional services released in 2015, particularly the likelihood of settlement image service, expand the utility of the WPE by allowing Esri’s users who possess census data or population estimates of higher quality than those used by Esri to produce higher-quality estimates of where people live. Some countries do not publicly release their most detailed census enumeration unit boundaries, but now ArcGIS users within those governments can produce higher-precision estimates of where their population may live by using the settlement likelihood service.
The WPE is a dasymetric portrayal, as opposed to an areal weighting or pycnophylactic interpolation, as described in Hay et al., (2005) that implements three smart interpolation methods for producing gridded population estimates. Linard and Tatem (2012) adjust this relationship by labeling smart interpolation as a fourth, more sophisticated approach involving imagery and multiple ancillary datasets. Thus, the WPE could be categorized as a dasymetric map using a smart interpolation method, though, as will be presented in the methodology section, the WPE is dasymetric first with smart interpolation applied afterward. Voinov (2014) presents arguments for the superiority of using a dasymetric technique to model the footprint of human settlement because assumptions about the distribution of population are made in the other methods.
The method for producing the WPE is different from other settlement pattern estimates in two ways. First, the unique combination of processing steps and the order of operations differ from other dasymetric settlement footprint estimates (Dobson et al., 2000; Pesaresi et al., 2012; Esch et al., 2013; European Statistical System, 2012; Linard and Tatem, 2012). Second, the model produces an estimate of the settlement footprint for nonurban areas using a technique to analyze panchromatic Landsat8 scenes to derive a measurement of likelihood of textural features (Haralick, Shanmugam, and Dinstein, 1973). Cheriyadat, et al., (2007) and Pesaresi, et al., (2012) implemented a textural features algorithm using a gray level co-occurrence matrix (GLCM), which is succinctly described in Albregtsen (2008) and Pradhan et al., (2013).
Esri’s model for producing the WPE begins with using a global 30-meter resolution classified land cover raster dataset called BaseVue 2013. The purpose is to establish known urban areas, areas likely to include settlement, and areas not likely to contain settlement. BaseVue is produced by MDA Information Systems LLC, a subsidiary of MacDonald Dettwiler and Associates Ltd (MDA, 2014). MDA derived BaseVue 2013 from Landsat8 scenes by using a classification and regression tree (CART) algorithm similar to Smith, Bolton, and Jengo (2004). BaseVue integrates the U.S. Geological Survey’s 2011 National Land Cover Dataset (NLCD) (Homer et al., 2015) within the conterminous United States.
The BaseVue 2013 land cover dataset contains sixteen Anderson-style land cover classes (Anderson, et al., 1976). Esri’s model begins by using the Reclassify tool from the ArcGIS Spatial Analyst software to assign an initial score to each BaseVue land cover class to represent the likelihood of people to live within a BaseVue cell with that respective class assignment. This includes a score of zero for cells with classes where people are not likely to live. Table 1 lists BaseVue’s classes and criteria (MDA, 2014) and Table 2 lists scores assigned by Esri’s model.
|1||Deciduous Forest||Trees > 3 meters in height, canopy closure > 35% (<25% intermixture with evergreen species) that seasonally lose their leaves, except larch|
|2||Evergreen Forest||Trees > 3 meters in height, canopy closure > 35% (<25% intermixture with deciduous species), of species that do not lose leaves (will include coniferous larch regardless of deciduous nature)|
|3||Scrub/Shrub||Woody vegetation < 3 meters in height, > 10% ground cover. Only collect > 30% ground cover.|
|4||Grassland||Herbaceous grasses, > 10% cover, including pastureland. Only collect > 30% cover.|
|5||Barren or Minimal Vegetation||Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water.|
|7||Agriculture, General||Cultivated cropland|
|8||Agriculture, Paddy||Cropland characterized by inundation for a substantial portion of the growing season|
|9||Wetland||Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species)|
|10||Mangrove||Coastal (tropical wetlands) dominated by mangrove species|
|11||Water||All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streams|
|12||Ice/Snow||Land areas covered permanently or nearly permanently with ice or snow|
|13||Clouds||Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunction|
|14||Woody Wetlands||Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with or covered by water. Only used within the continental U.S.|
|15||Mixed Forest||Areas dominated by trees generally greater than 5 meters tall and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S.|
|20||High Density Urban||Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for > 60%. Commercial, industrial, and transportation, e.g., train stations, airports.|
|21||Medium-Low Density Urban||Areas with 30% to 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation, e.g., train stations, airports.|
|Class ID||Class Name||Modeled Population Likelihood Score and Rationale|
|1||Deciduous Forest||25—Potentially Orchard Agriculture|
|4||Grassland||25—Potentially Range/Pasture Agricultural Land|
|5||Barren or Minimal Vegetation||0|
|20||High Density Urban||200|
|21||Medium-Low Density Urban||150|
There are two challenges with this reclassified BaseVue data. First, there are missing populated places, particularly smaller places such as rural villages and farms that did not have sufficient levels of constructed materials per MDA’s definitions for classes 20 and 21. Second, in the area with scores of 25, there is a great deal of unpopulated land. However, those areas also include populations living at the edges of cities or in rural areas, on farms, or in isolated residences.
To address the first issue of missing populated places, Esri used the GeoNames.org gazetteer database (GeoNames.org, 2013). Esri used a subset of GeoNames where the Category field contained a value of ‘PPL’, which GeoNames.org defines as ‘a city, town, village, or other agglomeration of buildings where people live and work’. The following steps were taken using ArcGIS 10.3.1 software to process the GeoNames.org data and add their locations to the reclassified BaseVue dataset:
The second problem of areas being included, particularly within agricultural areas where no people live, has a two-part solution. In the result of Step 5 above, any cell with a likelihood score of 25 needs to be evaluated to determine whether people are likely to be living there. Esri used two processes to accomplish this. The first was to use proximity to road intersections. Tang (2003) argues that road pattern is closely related to urban growth. Thus, Esri created a global dataset of road intersections from two sources: HERE.com (HERE.com, 2015) and OpenStreetMap (OSMF, 2015). A country-by-country analysis was conducted to determine which countries had more data from either source and therefore which source would be used. The vector line street data were assembled into datasets for six continental zones. To produce intersection points, Esri used the following processing steps:
To this point, the results contain scores of 150 or higher for areas that have a high certainty for representing places where people live. The cells with a score of 25 still represent mostly unpopulated locations but include farms and isolated residences. To screen out the areas where people are not living, Esri modeled panchromatic imagery from Landsat8 against the cells with a value of 25. The use of imagery for this purpose is common to all other country- or global-scale dasymetric settlement estimates (Cheriyadat et al., 2007; Pesaresi et al., 2012; Esch et al., 2013; European Statistical System, 2012; Linard and Tatem, 2012).
Esri’s method of evaluating Landsat8 (Esri, 2014a) imagery assesses whether a 5 × 5 cell moving neighborhood window is likely to contain textural features (Haralick, Shanmugam & Dinstein, 1973; Albregtsen, 2008) as opposed to explicitly modeling the features as discussed by Cheriyadat et al., (2007) and Pesaresi, et al., (2012). Esri used the following processing steps in ArcGIS to produce a likelihood score for textural features to use as a likelihood of settlement score:
Thus, the result of the above steps is a dasymetric surface representing settlement likelihood with values ranging from 0 to 300. Figure 3 illustrates how these steps work on one example neighborhood.
Any likelihood score above 150 represents at least low-medium residential population density. Any score above 200 represents high population density. Scores from 1 to 125 represent the potential for rural population density. Cells with a score of 0 represent no population living in that location. Statistically, the likelihood of settlement score can be represented as follows:
Where T represents the likelihood of texture score within a 5 × 5 cell neighborhood of each cell (C) in the Landsat8 panchromatic imagery.
T-Settlement level is the likelihood of texture score for a cell most likely to indicate human settlement. The mean (μ) and standard deviation (σ) are for the 0.5-degree processing tile.
Figure 3 also illustrates the process described in Steps 2–9 above, which produce the final dasymetric footprint of settlement scores representing a likelihood of settlement in those locations. To this point, the cells in the dasymetric settlement footprint represent the following conditions and value ranges:
The last part of Esri’s model to create the WPE apportions population estimate data, stored in polygons representing either census enumeration units or areas where population has been estimated or surveyed. The sources for these data are as follows, in order of preference:
The populations from these polygons are apportioned in a two-stage process to create a new raster dataset given the dasymetric settlement footprint with the likelihood scores as input. First, if there are more cells with nonzero settlement scores in a given polygon than the number of people, dasymetric settlement likelihood surface for that polygon is reprocessed as follows:
Generally, the above two steps are required for sparsely populated polygons representing large geographic areas. To complete the apportionment, the populations of the polygons are distributed to the cells based on each cell’s settlement likelihood score. This is done by determining the ratio of the polygon’s population to the sum of settlement scores inside the polygon.
The resulting population surface is verified as having the correct total population for all countries and then for each country when the raster cells whose centers fall within a given country’s boundary are extracted and summed. A population density surface is also computed, and locations with population densities over 50,000 persons per square kilometer are manually evaluated. Such areas occur due to horizontal inaccuracies within the population polygons, where they may have only cover or extend into locales where the dasymetric settlement likelihood surface contains too few cells. To address this, the topology of the population polygons is edited to match high-resolution satellite imagery (Esri 2015c), and the steps to apportion the population data are repeated.
To summarize, the method for determining the dasymetric settlement surface starts with known urban areas, adds highly likely locations near road intersections and GeoNames populated places, and then models the edges of settlement and fills in rural settlement using texture from panchromatic imagery. There are several aspects of this process worthy of further discussion:
BaseVue urban classifications: The Medium-Low Density Urban and High Density Urban classifications require 40% and 60%, respectively, of land area to be commercial, industrial, or transportation, implying large areas of concrete exist. Thus, villages or concentrations of people where there are no paved roads or modern infrastructure are classified using the land cover type of the surrounding area. This also indicates a conservative estimate of the size of urbanized areas. The outskirts of cities and suburban areas may not contain a sufficient level of visible pavement or structures to be considered in either of the urban classes. It is important that these urban classes in BaseVue not overestimate the geographic extent of the urban footprint (Linard et al., 2012). Therefore, the urban classes were isolated and visually inspected relative to high-resolution satellite imagery in several dozen cities, occurring on all continents, to verify this to be true. Thus, these two classes of BaseVue were deemed an appropriate starting point.
Modeling the likelihood of textural features: Cheriyadat, et al. (2007) discuss an approach to using grayscale imagery as a basis for detecting dasymetric settlement footprints. Their approach focuses on finding local edge patterns (LEP) using a GLCM. Among the challenges they discuss is the lack of guidance for the size of the neighborhood to analyze around each pixel. Cheriyadat, et al. (2007) reported using a 17 × 17 neighborhood within a given image, and 31 × 31 at the edges.
As described above, a 5 × 5 neighborhood was used to analyze 15-meter resolution Landsat8 panchromatic imagery. This approximately matches Pesaresi, et al., (2012), where 5 × 5 cell neighborhoods were applied to 10-meter resolution imagery, though the basis for that determination was to use a 50-meter window size regardless of resolution. This neighborhood size was useful because it covers roughly half a city block and would encompass at least part of a large building and either its shadow or the shadow from an adjacent building. This combination of shadows and high reflectance off building materials is conceptually the same as LEP described above, except instead of defining explicit cell value and distance pairings, a high value range of cell values must occur within the 5 × 5 cell neighborhood.
Using only the range for a 5 × 5 cell neighborhood will provide some insight into the likelihood of textural features. However, it does not eliminate the possibility of one-cell spikes, particularly off highly reflective building materials or bodies of water. For example, such a spike would be a value of 48,000 in the midst of cell values ranging 7–11,000. The high range of values in the neighborhood of this cell would be skewed. However, using the sum of ranges sufficiently dampens the impact and reduces the likelihood for that location to be above the mean plus the standard deviation of the processing tile’s sum of range values. This implies that two or more edges or textural features are within the 5 × 5 neighborhood of cells with a sum of value ranges higher than the mean plus the standard deviation for the processing tile. For comparison, Pesaresi, et al., (2012) used a minimum of four GLCM cell pairs occurring within a 5 × 5 neighborhood to constitute texture, thus showing that the count of cell pairs in a GLCM could also be used as a threshold.
Haralick, Shanmugam, and Dinstein (1973) presented textural features with the intent of locating and ultimately extracting features from a single image and presumed the values of grayness in the cells could be represented using pre-specified pairs of values, rather than ranges between any pair of cells. This makes sense if a feature can be fully defined using pairs of cells. The values of cells in the same location but having a different image date, or for the same type of structure (e.g., a McDonald’s restaurant) but in a different image location, will vary even if all imagery is from the same sensor. Thus, a method that is more tolerant of varying conditions is needed.
This idea of finding a relatively high range of cell values within a local neighborhood is not new. Here it has been adapted from terrain ruggedness or rugosity models found in the landscape ecology literature. Rugosity is a measurement of the roughness of terrain (normally quantified by a ratio of surface area to planar area) and is useful for describing the geomorphic conditions that potentially define major characteristics for plant and animal habitat (Beier, Majka, and Spencer, 2008; Jenness, 2004). Models for ruggedness analyze elevation datasets and often a slope derivative to classify how much change exists within a locale (Cooley, 2016). This can be applied to grayscale imagery, whereby high amounts of local changes in value may indicate landscape disturbance, settlement, or false positives such as sun glints on water bodies or the edges of natural or agriculturally vegetated areas. In producing the WPE, many of these false positives were inconsequential because they did not occur within urban or agricultural areas, and others were eliminated later when the lower textural values in sparsely populated areas were removed.
Areas to improve: This method could be improved in several areas:
One aspect of this method is not starting with or using any previously produced gridded population estimate, gridded settlement footprint estimate, or lights at night estimate. This affords the benefit of not including any of the potential errors or uncertainty of those works. However, each of these previous efforts also have strengths, and therefore this method derives no benefit from the existing gridded population datasets. It should also be noted that, as made apparent in the above section of areas to improve, many of the ancillary input datasets vary in spatial quality, and therefore, any efforts to model a dasymetric settlement surface using these datasets suffers from the uncertainty of where the quality varies.
The authors propose that the methodology described herein for determining a dasymetric settlement likelihood surface, starting with a conservative estimate of urban land cover and then adding to the footprint based on known locations of settlement, locations near road intersections, and the likelihood of two or more textural features within a 5 × 5 cell neighborhood of Landsat8 panchromatic imagery, is not only a viable alternative to the previously published smart interpolation methods for producing a dasymetric settlement footprint but may also be considerably faster. In particular, using a GLCM to confirm the presence of edges of textural features appears to require a great deal of computational resources. These resources are needed to test for specific conditions within potentially large spatial neighborhoods as opposed to the minimalist 5 × 5 cell neighborhood undertaken in this method.
MDA Information Systems LLC – BaseVue classified land cover data
U.S. Geological Survey – Landsat8 Data
Amazon AWS – S3 volume access for Landsat8 panchromatic data, and Landsat8 metadata
here.com – Streets and roads data
OpenStreetMap.org – Streets and roads data
GeoNames.org – Populated places data
Michael Bauer Research GmbH – Population estimates
Environics Analytics – Population estimates
United Nations Population Division – Population estimates
Esri – Population estimates
The authors have no competing interests to declare.
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