ArcGIS REST API

Apportionment reliability

Reliability is determined by assessing the quality of the census data and the quality of a weighted footprint of human settlement to enrich demographic information onto a given polygon.

Scoring determination

A country's reliability estimate represents the mean of three factors that affect the GeoEnrichment tool's ability to produce an accurate result. Each factor is treated as a statistical raster surface at a resolution of 1-km. The potential range is 1.0 (best) to 5.0 (worst), though no countries are rated with these extreme scores.

Below are the three factors:

Country's census reliability (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 TypeReliability Score

Census - de jure - complete tabulation

1

Census - de facto - complete tabulation

2

Estimate - de jure

3

Estimate - de facto

4

Sample survey - de facto

5

Completeness / Reliability Reliability Score

Final figure, complete

1

Final figure, incomplete/questionable reliability

2

Provisional figure

3

Provisional figure with questionable reliability

4

Age of Census or Estimate Reliability Score

1 - 2 years

1

3 - 4 years

2

5 - 6 years

3

7 - 8 years

4

9 - 10 years

5

Ratio of the population polygon to the number of people (1.0 – 5.0)

The larger the area of a census tabulation area, the less likely the specific locations where people live can reliably be found. For large areas with relatively low populations, this means the likelihood of correctly locating where those people live is even lower. The table below shows the combinations area and population density used to classify a census tabulation polygon’s reliability for the ratio of population to area.

Reliability matrix

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. However, because this model is 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.

Inherent biases

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 and the United States, have middling scores.

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.

http://geoenrich.arcgis.com/arcgis/rest/services/World/GeoEnrichmentServer/Geoenrichment/Countries/US?f=pjson

JSON response example 1
{
  "messages": [],
  "countries": [
    {
      "id": "US",
      "name": "United States",
      "abbr3": "USA",
      "altName": "UNITED STATES",
      "continent": "North America",
      "distanceUnits": "Miles",
      "esriUnits": "esriMiles",
      "defaultExtent": {
        "xmin": -178.48633078,
        "ymin": 18.8717424169,
        "xmax": -66.9076521618,
        "ymax": 71.403759084
      },
      "defaultDatasetID": "USA_ESRI_2018",
      "datasets": [
        "USA_ESRI_2018",
        "USA_ASR_2018",
        "USA_RMP_2018",
        "USA_ACS_2018",
        "Landscape"
      ],
      "hierarchies": [
        {
          "ID": "census",
          "alias": "Standard",
          "shortDescription": "This data source for the standard US data is ESRI. Vintage 2018.",
          "longDescription": "<p>This data source is provided by Esri Inc.</p><p>Esri offers comprehensive demographic, lifestyle segmentation, consumer spending, and business content for a variety of geographic levels in the United States for use in applications such as site selection, profiling customers, analyzing markets, evaluating competitors, identifying opportunities, and many more.</p><p><a href=\\'http://doc.arcgis.com/en/esri-demographics/data/us-intro.htm\\' target=\\'_blank\\'>Learn more.</a></p>",
          "datasets": [
            "USA_ESRI_2018",
            "USA_ACS_2018",
            "USA_ASR_2018",
            "USA_RMP_2018"
          ],
          "levelsInfo": {
            "geographyLevels": [
              "Entire Country",
              "States",
              "Counties",
              "ZIP Codes",
              "Block Groups",
              "CBSAs",
              "Census Tracts",
              "Cities and Towns (Places)",
              "Congressional Districts",
              "County Subdivisions",
              "DMAs"
            ]
          },
          "variablesInfo": {
            "categories": [
              "Age",
              "At Risk",
              "Behaviors",
              "Business",
              "Education",
              "Health",
              "Households",
              "Housing",
              "Income",
              "Jobs",
              "Key Facts",
              "Marital Status",
              "Policy",
              "Population",
              "Poverty",
              "Race",
              "Spending",
              "Supply and Demand",
              "Tapestry"
            ]
          },
          "populationToPolygonSizeRating": 2.191,
          "apportionmentConfidence": 2.576
        },
        {
          "ID": "landscape",
          "alias": "Landscape",
          "shortDescription": "This data source for the landscape US data is ESRI. Vintage 2012.",
          "longDescription": "<p>The Esri Landscape Layers are a collection of data, currently available for the United States, that are applicable to a wide range of uses such as biogeographic analysis, natural resource management, and land use and conservation planning.  There are map layers that describe the physical structure of the land, such as hydrography, soil characteristics, geologic units, and land surface forms.  Plus, there are a variety of map layers in the biological and climatological domains, such as ecological systems, evapotranspiration, and critical habitat and other protected areas.  The term “landscape” also refers to the recoverable resources and manmade features that influence how we use the land and water.  Coal bed methane basins, oil shale basins, agricultural potential, and infrastructure, such as pipelines and transmission lines are examples of these types of landscape layers.</p><p><a href=\\'http://arcg.is/WILhrp\\' target=\\'_blank\\'>Learn more.</a></p>",
          "datasets": [
            "Landscape"
          ],
          "levelsInfo": {
            "geographyLevels": [
              "st",
              "huc4",
              "huc8",
              "huc12",
              "cy",
              "huc10",
              "huc2",
              "huc6",
              "us"
            ]
          },
          "variablesInfo": {
            "categories": [
              "Landscape"
            ]
          },
          "populationToPolygonSizeRating": 2.191,
          "apportionmentConfidence": 2.576
        }
      ],
      "defaultDataCollection": "KeyGlobalFacts",
      "dataCollections": "",
      "defaultReportTemplate": "Demographic and Income Profile",
      "currencySymbol": "$",
      "currencyFormat": "$0;-$0"
    }
  ],
  "childResources": []
}

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.

http://geoenrich.arcgis.com/arcgis/rest/services/World/geoenrichmentserver/GeoEnrichment/enrich?studyareas=[{"address":{"text":" 102 Aqua Ct New Smyrna Beach FL 32168"}}]&studyareasoptions={"areaType": "NetworkServiceArea","bufferUnits": "Minutes","bufferRadii": [15],"travel_mode":"Walking"}&dataCollections=["KeyGlobalFacts"]&returngeometry=false&f=pjson

JSON response example 2
{
  "results": [
    {
      "paramName": "GeoEnrichmentResult",
      "dataType": "GeoEnrichmentResult",
      "value": {
        "version": "2.0",
        "FeatureSet": [
          {
            "displayFieldName": "",
            "fieldAliases": {
              "ID": "ID",
              "OBJECTID": "Object ID",
              "sourceCountry": "sourceCountry",
              "X": "X",
              "Y": "Y",
              "areaType": "areaType",
              "bufferUnits": "bufferUnits",
              "bufferUnitsAlias": "bufferUnitsAlias",
              "bufferRadii": "bufferRadii",
              "aggregationMethod": "aggregationMethod",
              "populationToPolygonSizeRating": "Population to polygon size rating for the country",
              "apportionmentConfidence": "Apportionment confidence for the country",
              "HasData": "HasData",
              "TOTPOP": "Total Population",
              "TOTHH": "Total Households",
              "AVGHHSZ": "Average Household Size",
              "TOTMALES": "Male Population",
              "TOTFEMALES": "Female Population"
            },
            "spatialReference": {
              "wkid": 4326,
              "latestWkid": 4326
            },
            "fields": [
              {
                "name": "ID",
                "type": "esriFieldTypeString",
                "alias": "ID",
                "length": 256
              },
              {
                "name": "OBJECTID",
                "type": "esriFieldTypeOID",
                "alias": "Object ID"
              },
              {
                "name": "sourceCountry",
                "type": "esriFieldTypeString",
                "alias": "sourceCountry",
                "length": 256
              },
              {
                "name": "X",
                "type": "esriFieldTypeDouble",
                "alias": "X"
              },
              {
                "name": "Y",
                "type": "esriFieldTypeDouble",
                "alias": "Y"
              },
              {
                "name": "areaType",
                "type": "esriFieldTypeString",
                "alias": "areaType",
                "length": 256
              },
              {
                "name": "bufferUnits",
                "type": "esriFieldTypeString",
                "alias": "bufferUnits",
                "length": 256
              },
              {
                "name": "bufferUnitsAlias",
                "type": "esriFieldTypeString",
                "alias": "bufferUnitsAlias",
                "length": 256
              },
              {
                "name": "bufferRadii",
                "type": "esriFieldTypeDouble",
                "alias": "bufferRadii"
              },
              {
                "name": "aggregationMethod",
                "type": "esriFieldTypeString",
                "alias": "aggregationMethod",
                "length": 256
              },
              {
                "name": "populationToPolygonSizeRating",
                "type": "esriFieldTypeDouble",
                "alias": "Population to polygon size rating for the country"
              },
              {
                "name": "apportionmentConfidence",
                "type": "esriFieldTypeDouble",
                "alias": "Apportionment confidence for the country"
              },
              {
                "name": "HasData",
                "type": "esriFieldTypeInteger",
                "alias": "HasData"
              },
              {
                "name": "TOTPOP",
                "type": "esriFieldTypeDouble",
                "alias": "Total Population",
                "fullName": "KeyGlobalFacts.TOTPOP",
                "component": "demographics",
                "decimals": 0,
                "units": "count"
              },
              {
                "name": "TOTHH",
                "type": "esriFieldTypeDouble",
                "alias": "Total Households",
                "fullName": "KeyGlobalFacts.TOTHH",
                "component": "demographics",
                "decimals": 0,
                "units": "count"
              },
              {
                "name": "AVGHHSZ",
                "type": "esriFieldTypeDouble",
                "alias": "Average Household Size",
                "fullName": "KeyGlobalFacts.AVGHHSZ",
                "component": "scripts",
                "decimals": 2,
                "units": "count"
              },
              {
                "name": "TOTMALES",
                "type": "esriFieldTypeDouble",
                "alias": "Male Population",
                "fullName": "KeyGlobalFacts.TOTMALES",
                "component": "demographics",
                "decimals": 0,
                "units": "count"
              },
              {
                "name": "TOTFEMALES",
                "type": "esriFieldTypeDouble",
                "alias": "Female Population",
                "fullName": "KeyGlobalFacts.TOTFEMALES",
                "component": "demographics",
                "decimals": 0,
                "units": "count"
              }
            ],
            "features": [
              {
                "attributes": {
                  "ID": "0",
                  "OBJECTID": 1,
                  "sourceCountry": "US",
                  "X": -80.94857302553523,
                  "Y": 29.03368152986305,
                  "areaType": "NetworkServiceArea",
                  "bufferUnits": "Minutes",
                  "bufferUnitsAlias": "Walk Time Minutes",
                  "bufferRadii": 15,
                  "aggregationMethod": "BlockApportionment:US.BlockGroups",
                  "populationToPolygonSizeRating": 2.191,
                  "apportionmentConfidence": 2.576,
                  "HasData": 1,
                  "TOTPOP": 198,
                  "TOTHH": 122,
                  "AVGHHSZ": 1.62,
                  "TOTMALES": 92,
                  "TOTFEMALES": 105
                }
              }
            ]
          }
        ]
      }
    }
  ],
  "messages": []
}

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.