Finding Hot Spots using ArcGIS Online - Minimizing the ... · 2017 Esri Federal GIS Conference--Presentation, 2017 Esri Federal GIS Conference, Finding Hot Spots using ArcGIS Online

Finding Hot Spots in ArcGIS

Online: Minimizing the

Subjectivity of Visual AnalysisNicholas M. Giner – Esri

Parrish S. Henderson – FBI

Agenda

• The “subjectivity” of maps

• What is Hot Spot Analysis?

• Why do Hot Spot Analysis?

• Pattern Analysis: ArcGIS Desktop vs. ArcGIS Online

• Heat Maps vs. Hot Spot Maps

• How does Hot Spot Analysis work?

• How does Local Outlier Analysis work?

• Parrish Henderson – Federal Bureau of Investigation

Maps are subjective…

Bronx, NY Water Quality IncidentsJanuary 2010 – February 2017

Is there a spatial pattern in the location of these water quality incidents?

Maps are subjective…

Ft. Worth, TX Fire Response Times

Is there a spatial pattern in the response times of these fire calls?

Maps are subjective…Southeast USA Economic Resilience to Natural Disasters (Counties) - 2010

Natural Breaks

Equal Interval

Quantile

Geometric Interval

Is there a spatial pattern in economic resilience to natural disasters in the Southeastern USA?

What is Hot Spot Analysis?

• Identifies statistically significant hot spots and cold spots (i.e. spatial clusters of high and low values) in geographic data

• It is based on Tobler’s First Law of Geography – “all things are related, but near things are

more related than distant things” (i.e. Spatial dependence or Spatial autocorrelation)

Why do Hot Spot Analysis?

1) Exploration: To reveal new insights and quantify patterns in

data that you might not “see”

2) Regression workflows: Determining if you have spatial

dependence in your residuals (one of the “6 checks of

Ordinary Least Squares Regression”)

3) Interpolation workflows: Spatial dependence is the foundation

of Geostatistics

Pattern Analysis: ArcGIS Desktop vs.

ArcGIS Online

ArcGIS Online ArcGIS Pro

Heat Maps vs. Hot Spot Maps

Demo #1Heat Maps: Calculate Density in ArcGIS Online

How does Hot Spot Analysis work?

Getis-Ord Gi* Statistic



Feature


Feature

Neighborhood


Study Area

Feature

Neighborhood


Study Area

Neighborhood

Is this…

Significantly different

from the study area?


If significantly higher…

The feature is marked a Hot Spot

Hot Spot – 99% Confidence



Cold Spot – 90% Confidence



Not Significant













Not Significant

How is “Neighborhood” defined?

In ArcGIS Online, neighborhood is chosen for you via the Optimized Hot Spot Analysis tool

Distance

z-s

core

(d

egre

e o

f clu

steri

ng)

Spatial Autocorrelation is calculated at multiple distances

1

2

3

4

5

6

20 6040 80 100 120 140

“Optimal” neighborhooddistance is where degree of clustering is highest

Examples of Hot Spot Analysis

Chicago

Crimes:

(2014)

DC Snow Removal

Complaints:

Jan 2016 –

February 2016

NYC

Graffiti:

Jan 2010-

Present

Point Locations

Examples of Hot Spot AnalysisPoint Attributes

Austria Heavy Metals:

Cadmium Concentration

Examples of Hot Spot AnalysisPolygon Attributes

Philadelphia Tracts:

Market Potential for MedicaidDC Block Groups:

Republican Party Affiliation

Demo #2Hot Spots: Find Hot Spots in ArcGIS Online

How does Local Outlier Analysis work?

Local Indicators of Spatial Association (LISA) Statistic



Feature


Feature

Neighborhood


Neighborhood

Is this…


from all other neighborhoods?

AND


from all other features?

Feature

Is this…


HH

LL LH

Feature is higherthan other features,

Neighborhood is higherthan other neighborhoods

Feature is higherthan other features,

Neighborhood is lowerthan other neighborhoods

Feature is lowerthan other features,

Neighborhood is lowerthan other neighborhoods

Feature is lowerthan other features,

Neighborhood is higherthan other neighborhoods

High Outlier

Low Outlier

HL


Not Significant

Low-High Outlier

High-Low Outlier

High-High Cluster

Low-Low Cluster

Demo #3Outliers: Find Outliers in ArcGIS Online

Analyzing Crime Hot

Spots

in New OrleansParrish Henderson – Federal Bureau of Investigation

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Appendix

Equations

is a disaggregated version of the global Moran’s statistic in that the equation is applied only to one particular

zone rather than the summation of all the zones in the dataset (O’Sullivan and Unwin, 2003). Thus it is a “local”

measure of spatial autocorrelation and attempts to identify statistically significant clusters of similar values (high or low

values). Because the LISA statistic is calculated locally, the Local Moran’s index scores can be displayed spatially on a

cluster map (Anselin, 1995). What is the definition of zone?

In this case, the “zone” is defined by the adjacency matrix and the order of contiguity specified. For example, a first-

order, Queen’s case contiguity “zone” around the state of Pennsylvania would include all states immediately sharing a

border on all sides with Pennsylvania (Ohio, West Virginia, Maryland, Delaware, New Jersey, and New York). The “zone”

would change if the type and order of contiguity changed. Other examples of the type of contiguity would be the

Rook’s and Bishop’s case. Other examples of order of contiguity would be second or third order (Ord, 2010).

where zi and zj represent deviations from the mean in zones i and j, and the summation over j indicates that onlyneighboring values are included in the calculation. W is the spatial weights matrix and defines adjacencies (Anselin,

1995).

are local measures of spatial autocorrelation that calculate the degree of association between zone i and its

neighbors j given a specified distance radius d (Getis and Ord, 1992). They help identify pockets of

dependence called hot spots or cold spots. For example, if zone i has a high value and its neighbors j within

distance d have high values, then this is a “hot spot”. The null hypothesis for these statistics is that there is no

spatial clustering of similar values and the output is a map of z-scores. If a z-score is high or low (+ or – 1.96

for a p-value < 0.05) then we can reject the null hypothesis and conclude that there is statistically significant

spatial clustering of high or low values.

where d represents the distance radius, the numerator is the sum of all zones j within d of zone i, and the

denominator is the sum of all zones j in the study area not including zone i (Getis and Ord, 1992). The only

difference between Gi and Gi* is that Gi* includes the value of zone i in the calculation (O’Sullivan and Unwin,

2003).

z-scores and p-values

Documents

Finding Hot Spots using ArcGIS Online - Minimizing the ... · 2017 Esri Federal GIS Conference--Presentation, 2017 Esri Federal GIS Conference, Finding Hot Spots using ArcGIS Online