Hot Spot Analysis

[<– 2 Anselin Moran’s I](

Also known as Getis-Ord Gi* – The resultant z-scores and p-values tell you where features with either high or low values cluster spatially. This tool works by looking at each feature within the context of neighboring features. A feature with a high value is interesting by may not be a statistically significant hot spot. To be a statistically significant hotspot, a feature will have a high value and be surrounded by other features with high values as well. The local sum for a feature and its neighbors is compared proportionally to the sum of all features; when the local sum is very different from the expected local sum, and that difference is too large to be the result of random choice, a statistically significant z-score results.

The Gi* statistic returned for each feature in the dataset is a z-score. For statistically significant positive z-scores, the larger the z-score is, the more intense clustering of high values (hot spot). For statistically significant negative z-scores, the smaller the z-score is, the more intense the clustering of low values (cold spot).

When to use: Results aren’t reliable with less than 30 features. Applications can be found in crime analysis, epidemiology, voting pattern analysis, economic geography, retail analysis, traffic incident analysis, and demographics.

Examples: Where is the disease outbreak concentrated? – Where are kitchen fires a larger than expected proportion of all residential fires? – Where should the evacuation sites be located? – Where/When do peak intensities occur?

How to use:

Step 1: Add US_Income feature class to a new ArcMap document from the sample data.

Step 2: Select Hot Spot Analysis tool (ArcToolbox > Spatial Statistics > Mapping Clusters > Hot Spot Analysis (Getis-Ord Gi*))

Step 3: Fill in the fields as specified below:

  • Input Field: B34_2008 is the average income wage per county
  • Output Feature Class: We’ll output the feature class to SampleData.gdb and call itUS_Income_Hotspots
  • Conceptualization of Spatial Relationships: see tool help for explanation of each conceptualization. For this exercise we will use the fixed distance band conceptualization through which each feature is analyzed within the context of all of its neighboring features
  • Distance Method: either straight line (Euclidean) or at right angles (Manhattan). We’ll use Euclidean distance
  • Standardization: standardize spatial weights if spatial distribution of features is biased. Leave as ‘None’
  • Distance Band: specifies a cutoff distance for Inverse Distance and Fixed Distance options. Leave blank
  • Self Potential Field: the field representing self-potential. The distance or weight between a feature and itself. Leave blank

Step 4: Hit OK and let the tool run. After the tool completes running, a new feature class will be created and added to the Table of Contents similar to below:

This tool creates a new Output Feature Class with a z-score and p-value for each feature. In this example, the red counties represent the hotspots and the blue counties represent coldspots. The Great Plains are looking a little chilly!

4 Average Nearest Neighbor –>