GeoEnrichment and Living Atlas

Demographic DataCharlie Frye and Mark Gilbert

ArcGIS Living Atlas of the World Team, Esri Redlands

Istanbul, Turkey

The Living Atlas of the World

• The foremost collection of geographic information from around the globe. It includes

layers, maps, apps, packages and more.

• Using it is as simple as adding data. It is ready to use in every Pro project.

• Sessions:

- The Living Atlas of the World: An Introduction - Wednesday (Today) 1:00 – 2:00 SDDC Room 14-B

- Living Atlas of the World: The Road Ahead – Wednesday (Today) 4:00 – 5:00 SDDC Room 16-B

- Search Agenda for “Living Atlas”

It’s a folder containing all the best GIS data in the world

https://livingatlas.arcgis.com

The ArcGIS

Living Atlas of the World

How the GeoEnrichment Process Works

Step 1: User supplies

• A polygon to enrich

• List of attributes

(variables) they want to

add to their polygon.

This example is within Lisbon, Portugal

How the GeoEnrichment Process Works

Step 2: ArcGIS selects

the most detailed census

polygons (outlined in

orange) that intersect the

Enrichment Polygon

(purple ring)

How the GeoEnrichment Process Works

Step 3: ArcGIS selects

the subset of most

detailed census polygons

completely inside the

Enrichment Polygon and

summarizes the

attributes for these

polygons.

How the GeoEnrichment Process Works

Step 4: Apportionment

1. Using a grid of

“Settlement Points” with

values of representing the

likelihood of people

living nearby.

Settlement Points are spaced

on a 75-m grid

How the GeoEnrichment Process Works

Step 4: Apportionment

2. For census polygons

intersecting the

enrichment polygon

boundary, determine an

inside vs. outside

weighting using the

settlement points

likelihood score

3. Summarize weighted

variables and add to

summary from step 3.

How the GeoEnrichment Process Works

In addition to regularly spaced

points from Esri’s settlement

score model, Esri adds

centroids (black points) for

any tiny reporting units (with

orange outlines) that do not

contain a settlement point.

Thus, the settlement point

data for a given country

includes both modeled points

and points for these tiny

reporting units.

Settlement Score Methods

• Census Block Points—U.S. and Canada only. These points are initially produced as

centroids from the most detailed census tabulation areas.

• Settlement Points—For most other countries Esri produces settlement points based on a

settlement likelihood model that uses Landsat8 imagery and road intersections. Esri

published the methodology for modeling settlement scores in the Data Science Journal.

• Address Based Settlement Points—Switzerland and Netherlands only. Esri aggregates

the count of these address points in to a 75-meter resolution raster and converts that to a

point dataset like settlement points.

• Described further in the GeoEnrichment API’s Data Apportionment help topic.

Poland

Urban vs. Rural

Apportionment Method

• Normally the Settlement points described earlier are used as a basis for apportionment

• Enriching large polygons uses the centroids of intermediate levels of geography

• In order of priority:

- Settlement Points – Called Block Apportionment

- Centroids of next most-detailed level of geography

- U.S.: from adjusted Block centroids to block group centroids, then tract centroids

- Romania: from settlement to postal code level-6 with settlement points, then postal code 5 with level-6

centroids.

• If the polygon being enriched is too small, i.e., does not contain even one settlement

point or centroid, then no result is returned, and no credits will be used.

Level of Geography

• Each Country has its own unique basis to support GeoEnrichment

- Detailed census tabulation unit boundaries

- AU, BR, CA, CZ, ES, FR, IT, JP, NL PT, SI, UK, US, and ZA

- Detailed postal code boundaries

- RO (PC6), AT (PC4), BE (PC4), BG (PC4), CH (PC6), DE (PC), DK (PC4), EE (PC5), FI (PC5),HU (PC4),

IE (elect), IN (subDist), LT (PC5), NO (PC4), NZ (AU), PL(PC5), SE (PC5), and TR (PC5)

- Remaining 100 countries have municipal or coarser boundaries

Reliability Scores

• Two issues affect the reliability of GeoEnrichment results:

- Quality of Census data

- Age of last true census and reliability of that or the most recent survey

- Type of Census (de-jure vs. de-facto)

- Size of reporting units (large = poor results, small = best results)

- Quality of Footprint for Settlement Score

- Complexity of Footprint

- Quality of Landsat8 Imagery

• Two Reliability Scores:

- Overall

- Ratio of population to reporting unit size

Available in ArcGIS Online Results

Reliability Example:

Lisbon:

• 183 detailed polygons from

census data.

- 114 inside

- 69 on perimeter

• High quality because census

data to drives results

Reliability Example:

Jakarta

• Forces assumptions of

homogeneity in

apportionment.

- 5 not so detailed polygons

from census data intersect

perimeter

- Purple ring area is 100 times

larger than Lisbon’s

• Low quality because census

data cannot indicate detailed

changes in density

•

GeoEnrichment Patterns of Use

• Options for input data:

- Your existing Feature Service or Feature Class

- AGOL Add map notes layer with one point and use Travel Time option

Rome, Italy

GeoEnrichment Patterns of Use

• Options for input data:

- Your existing Feature Service or Feature Class

- AGOL - Add map notes layer with one point and use Travel Time option

- Pro - Derive special area from a Living Atlas raster layer

Forest Dwellers in

Marin County,

California, USA

GeoEnrichment Patterns of Use

• Options for input data:

- Your existing Feature Service or Feature Class

- AGOL - Add map notes layer with one point and use Travel Time option

- Pro - Derive special area from a Living Atlas raster layer

- Gridded versus individual area

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