https://geoenrich.arcgis.com/arcgis/rest/services/World/GeoEnrichmentServer/Geoenrichment/Countries/GR?f=pjsonApportionment reliability for raster-based settlement points
The reliability of any GeoEnrichment using raster-based settlement points is determined by the quality of each country's census from which population and other demographic variables are derived and the quality of a weighted footprint of human settlement used to apportion the selected GeoEnrichment variables onto a given polygon.
Each country using raster-based settlement points is given two scores:
- Area to population reliability which focuses only on the size (area) of the census polygon and the number of people estimated to live in that polygon.
- The overall reliability score which encompasses all aspects of reliability and includes the area to population score.
Area to population reliability
There are currently two methods for calculating each of these. The older method is simpler and beginning with the countries released in the October 2020 and later, a more robust method is used.
Mean of scores for polygon size and level of population (1.0 – 5.0)
We assign reliability as follows:
| Population of a given census polygon | Reliability Score |
|---|---|
Over 100,001 | 100 |
10,001 to 100,000 | 25 |
2,501 to 10,000 | 10 |
501 to 2500 | 5 |
101 to 500 | 2 |
Less than 100 | 1 |
| Area of a given census polygon | Reliability Score |
|---|---|
Over 100.1 Km2 | 100 |
16.1 to 100 Km2 | 25 |
4.1 to 16.0 Km2 | 10 |
1.1 to 4.0 Km2 | 5 |
0.11 to 1.0 Km2 | 2 |
Less than 0.1 Km2 | 1 |
The mean of these scores is then scaled using a simple linear equation to a range of 1.0 (best) to 5.0 (worst).
Overall reliability
We combine Area to Population Reliability with Complexity of the weighted footprint of human settlement and Census Quality reliability scores from The United Nations Statistical Division.
Complexity of settlement footprint relative to NoData and zero population cells (1.0 to 5.0)
Esri processes Landsat8 panchromatic imagery (15-meter resolution) to find texture. When levels of texture are sufficiently high, the likelihood that it represents human settlement is high. Worldpop.org uses a similar approach. However, because these models are largely completed using raster data, underestimation of the footprint edges occur due to resampling. The amount of area is proportional to the complexity of the (raster) human settlement footprint. Complexity is measured as the sum of distances from a given cell to all NoData cells within 8 kilometers (this figure is then scaled to 1.0 to 5.0). NoData occurs at coastlines and where low amounts of texture from Landsat8 imagery are located.
Census Quality reliability scores (1.0 - 5.0)
The United Nations Statistical Division reports characteristics for each country’s census with respect to completeness, age of the most recent census and subsequent estimates used to derive the current estimate, and the type of census (de-jure vs. de facto). Examples of these scores can be found on the U.N. Statistical Division’s website. Esri scores each of these characteristics as shown below and produces a combined mean score that is represented as a constant value everywhere in the country.
| Census Type | Reliability Score |
|---|---|
Census - de jure - complete tabulation | 1 |
Estimate - de jure | 2 |
Census - de facto - complete tabulation | 3 |
Estimate - de facto | 4 |
Sample survey - de facto | 5 |
| Completeness / Reliability | Reliability Score |
|---|---|
Final figure, complete | 1 |
Provisional figure | 2 |
Final figure, incomplete/questionable reliability | 5 |
Provisional figure with questionable reliability | 5 |
| Age of Census or Estimate | Reliability Score |
|---|---|
1 - 2 years (rarely available this soon) | 1 |
3 years | 2 |
3 - 5 years | 3 |
5 - 6 years | 4 |
6 - 10 years | 5 |
More detailed information about the data sources and methods for each country can be found on the Esri Demographics Global data overview page.
Because of the varied nature of each country and how census information is collected, there are some biases in this score. In general:
- Small countries tend to have better scores.
- Countries with small, similarly-sized tabulation units, have better scores.
- Countries with a wide variety of tabulation geography sizes-for example, Saudi Arabia or India, have middling scores.
Two metrics are used to calculate scores, how settlement points are used is explained further in Data Apportionment.
- Percent of Area Settled—This is determined using a raster analysis at 75-m resolution where the count of cells in the footprint of settlement is divided by the count of cells representing the census polygon.
Percent of Area Settled Reliability Score Less than 0.1%
100
0.1 to 4.99%
25
5.0 to 19.99%
10
20.0 to 49.9%
5
50.0 to 98.9%
2
99.0 to 100%
1
- Ratio of Settlement points (centroids of 75-m raster cells) to population—Depending on how settlement points were derived, especially those not based on address points or building footprint centroids, it is possible to have extra points representing buildings people do not live in, or other landscape disturbances Esri's algorithm may mistake for human settlement. Ideally each settlement point represents where 15-50 people live.
Ratio of population to settlement point Reliability Score Less than 2.9
Over 500
100
3.0 to 6.9
200.1 to 500
25
7.0 to 11.9
100.1 to 200
10
12.0 to 17.9
50.1 to 100
5
18.0 to 23.9
36.1.to 50.0
2
Between 24-36
1
The census quality reliability scores for census type and completeness remain the same, while age of census was modified as follows in the new method:
| Age of Census or Estimate | Reliability Score |
|---|---|
1 - 2 years (rarely available this soon) | 1 |
3 - 5 years | 3 |
6 - 20+ years | 5 |
Applying the reliability score
The reliability score can be used to estimate the smallest polygon that can be accurately enriched in a given country. To perform this estimate, you square the reliability score and multiply by three to derive the number of square kilometers a polygon's area will need to expect the best quality results when enriching polygons in that country. The reliability score can be modified by up to a 1.0 depending on whether the polygon to be enriched is covering an urban area or a rural area. Subtract 1.0 if it is an urban area because census data, generally, is more reliable in urban areas. Conversely, add 1.0 for rural areas.
As the reliability score increases (becomes poorer), so must the size of the enriched polygons in order to obtain reliable results.
Tip:
It is important to remember that while a country's reliability may be average or even poor, there may be areas of better reliability within the country which could reliably support enriching smaller polygons.
Example usage
The reliability score values are determined on a per country basis. This makes them easily discoverable using the Countries query method.
Request example 1
In order to find the reliability estimates for each country, you can use the Countries discovery method. The values that represent the reliability estimates are: populationToPolygonSizeRating and apportionmentConfidence.
https%3A%2F%2Fgeoenrich.arcgis.com%2Farcgis%2Frest%2Fservices%2FWorld%2FGeoEnrichmentServer%2FGeoenrichment%2FCountries%2FGR%3Ff%3Dpjson
{
"messages" : [ ],
"childResources" : [ ],
"countries" : [ {
"id" : "GR",
"name" : "Greece",
"abbr3" : "GRC",
"altName" : "GREECE",
"continent" : "Europe",
"distanceUnits" : "Kilometers",
"esriUnits" : "esriKilometers",
"defaultExtent" : {
"xmin" : 19.3725059703,
"ymin" : 34.8012039371,
"xmax" : 29.6066369556,
"ymax" : 41.7485377735,
"spatialReference" : {
"wkid" : 4326,
"latestWkid" : 4326
}
},
"defaultDatasetID" : "GRC_MBR_2021",
"datasets" : [ "GRC_MBR_2021" ],
"hierarchies" : [ {
"ID" : "census",
"alias" : "GR Standard Data",
"shortDescription" : "This data source for the standard GR data is Michael Bauer Research (MBR). Vintage 2021.",
"default" : true,
"longDescription" : "This data source for the standard GR data is Michael Bauer Research (MBR). Vintage 2021.",
"locales" : [ "en" ],
"datasets" : [ "GRC_MBR_2021" ],
"levelsInfo" : {
"geographyLevels" : [ "Country", "Decentralized Administrations", "Postcodes1", "Regions", "Postcodes2", "Regional Units", "Postcodes3", "Municipalities", "Postcodes5" ]
},
"variablesInfo" : {
"dataBrowserVariableCount" : 125,
"categories" : [ "Age", "Education", "Households", "Income", "Jobs", "Key Facts", "Marital Status", "Population", "Spending" ]
},
"populationToPolygonSizeRating" : 2.19,
"apportionmentConfidence" : 2.29,
"apportionmentThresholds" : [ {
"method" : "BlockApportion",
"dataLayer" : "GR.Postcodes5",
"pointsLayer" : "GR.BlockPoints",
"maximumSize" : 150.0
}, {
"method" : "CentroidsInPolygon",
"dataLayer" : "GR.Postcodes5",
"maximumSize" : 250.0
}, {
"method" : "CentroidsInPolygon",
"dataLayer" : "GR.Municipalities"
} ],
"hasInterestingFactsStatistics" : true
} ],
"defaultDataCollection" : "KeyGlobalFacts",
"defaultReport" : "GreeceSummaryReport",
"defaultReportTemplate" : "Greece Summary Report",
"defaultInfographic" : "key-facts-global",
"defaultInfographicTemplate" : "Key Facts (MBR)",
"currencyCode" : "EUR",
"currencyName" : "Euro",
"currencySymbol" : "€",
"currencyFormat" : "€0;-€0",
"dataCollections" : ""
} ]
}
Notes:
Request example 2
The values representing reliability estimates are also returned in the results of the enrich method. The names of the variables are the same as in the Countries discovery method: populationToPolygonSizeRating and apportionmentConfidence.
https://geoenrich.arcgis.com/arcgis/rest/services/World/geoenrichmentserver/GeoEnrichment/enrich?studyareas=[{"address":{"text":"Pireos 84, Athina 104 35, Greece"}}]&studyareasoptions={"areaType": "NetworkServiceArea","bufferUnits": "Minutes","bufferRadii": [15],"travel_mode":"Walking"}&dataCollections=["KeyGlobalFacts"]&returngeometry=false&f=pjsonhttps%3A%2F%2Fgeoenrich.arcgis.com%2Farcgis%2Frest%2Fservices%2FWorld%2Fgeoenrichmentserver%2FGeoEnrichment%2Fenrich%3Fstudyareas%3D%5B%7B%22address%22%3A%7B%22text%22%3A%22Pireos%2084%2C%20Athina%20104%2035%2C%20Greece%22%7D%7D%5D%26amp%3Bstudyareasoptions%3D%7B%22areaType%22%3A%20%22NetworkServiceArea%22%2C%22bufferUnits%22%3A%20%22Minutes%22%2C%22bufferRadii%22%3A%20%5B15%5D%2C%22travel_mode%22%3A%22Walking%22%7D%26amp%3BdataCollections%3D%5B%22KeyGlobalFacts%22%5D%26amp%3Breturngeometry%3Dfalse%26amp%3Bf%3Dpjson
{
"results" : [ {
"paramName" : "GeoEnrichmentResult",
"dataType" : "GeoEnrichmentResult",
"value" : {
"version" : "2.0",
"FeatureSet" : [ {
"displayFieldName" : "",
"fieldAliases" : {
"ID" : "Id",
"OBJECTID" : "Object ID",
"sourceCountry" : "Country code",
"X" : "X",
"Y" : "Y",
"areaType" : "Area type",
"bufferUnits" : "Buffer units",
"bufferUnitsAlias" : "Buffer units alias",
"bufferRadii" : "Buffer radii",
"aggregationMethod" : "Aggregation method",
"populationToPolygonSizeRating" : "Population to polygon size rating for the country",
"apportionmentConfidence" : "Apportionment confidence for the country",
"HasData" : "Has data",
"TOTPOP" : "Total Population",
"TOTHH" : "Total Households",
"AVGHHSZ" : "Average Household Size",
"TOTMALES" : "Male Population",
"TOTFEMALES" : "Female Population"
},
"spatialReference" : {
"wkid" : 4326,
"latestWkid" : 4326
},
"fields" : [ {
"name" : "ID",
"alias" : "Id",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "OBJECTID",
"alias" : "Object ID",
"type" : "esriFieldTypeOID"
}, {
"name" : "sourceCountry",
"alias" : "Country code",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "X",
"alias" : "X",
"type" : "esriFieldTypeDouble"
}, {
"name" : "Y",
"alias" : "Y",
"type" : "esriFieldTypeDouble"
}, {
"name" : "areaType",
"alias" : "Area type",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "bufferUnits",
"alias" : "Buffer units",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "bufferUnitsAlias",
"alias" : "Buffer units alias",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "bufferRadii",
"alias" : "Buffer radii",
"type" : "esriFieldTypeDouble"
}, {
"name" : "aggregationMethod",
"alias" : "Aggregation method",
"type" : "esriFieldTypeString",
"length" : 256
}, {
"name" : "populationToPolygonSizeRating",
"alias" : "Population to polygon size rating for the country",
"type" : "esriFieldTypeDouble"
}, {
"name" : "apportionmentConfidence",
"alias" : "Apportionment confidence for the country",
"type" : "esriFieldTypeDouble"
}, {
"name" : "HasData",
"alias" : "Has data",
"type" : "esriFieldTypeInteger"
}, {
"name" : "TOTPOP",
"alias" : "Total Population",
"type" : "esriFieldTypeDouble",
"fullName" : "KeyGlobalFacts.TOTPOP",
"component" : "demographics",
"decimals" : 0,
"units" : "count"
}, {
"name" : "TOTHH",
"alias" : "Total Households",
"type" : "esriFieldTypeDouble",
"fullName" : "KeyGlobalFacts.TOTHH",
"component" : "demographics",
"decimals" : 0,
"units" : "count"
}, {
"name" : "AVGHHSZ",
"alias" : "Average Household Size",
"type" : "esriFieldTypeDouble",
"fullName" : "KeyGlobalFacts.AVGHHSZ",
"component" : "scripts",
"decimals" : 2,
"units" : "count"
}, {
"name" : "TOTMALES",
"alias" : "Male Population",
"type" : "esriFieldTypeDouble",
"fullName" : "KeyGlobalFacts.TOTMALES",
"component" : "demographics",
"decimals" : 0,
"units" : "count"
}, {
"name" : "TOTFEMALES",
"alias" : "Female Population",
"type" : "esriFieldTypeDouble",
"fullName" : "KeyGlobalFacts.TOTFEMALES",
"component" : "demographics",
"decimals" : 0,
"units" : "count"
} ],
"features" : [ {
"attributes" : {
"ID" : "0",
"OBJECTID" : 1,
"sourceCountry" : "GR",
"X" : 23.718019958454192,
"Y" : 37.97983998804345,
"areaType" : "NetworkServiceArea",
"bufferUnits" : "Minutes",
"bufferUnitsAlias" : "Walk Time Minutes",
"bufferRadii" : 15,
"aggregationMethod" : "BlockApportionment:GR.Postcodes5;PointsLayer:GR.BlockPoints",
"populationToPolygonSizeRating" : 2.19,
"apportionmentConfidence" : 2.29,
"HasData" : 1,
"TOTPOP" : 47666,
"TOTHH" : 21576,
"AVGHHSZ" : 2.21,
"TOTMALES" : 22715,
"TOTFEMALES" : 24951
}
} ]
} ]
}
}
}
Notes:
- Reliability estimates cannot be used if the studyAreas, being used for analysis, have areas that include parts of more than one country. The values: populationToPolygonSizeRating and apportionmentConfidence will have NULL values as a result.