What is Getis-Ord G?
The High/Low Clustering (Getis-Ord General G) statistic is an inferential statistic, which means that the results of the analysis are interpreted within the context of the null hypothesis. The null hypothesis for the High/Low Clustering (General G) statistic states that there is no spatial clustering of feature values.
What does hot spot analysis do?
Hotspot Analysis uses vectors to identify locations of statistically significant hot spots and cold spots in your data by aggregating points of occurrence into polygons or converging points that are in proximity to one another based on a calculated distance.
What is kernel density Arcgis?
The Kernel Density tool calculates the density of features in a neighborhood around those features. It can be calculated for both point and line features. Possible uses include finding density of houses, crime reports, or roads or utility lines influencing a town or wildlife habitat.
What is the difference between kernel density and hot spot analysis?
Performed kernel density analyses are able to tell us where clusters in our data exist. Hot spot analysis considers a feature (e.g. crime event) in the whole dataset. A feature has a value or, in case of crime events, features are aggregated and their count within the aggregation area represents the value.
What are hotspots in GIS?
A hotspot can be defined as an area that has higher concentration of events compared to the expected number given a random distribution of events.
What does kernel density tell you?
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.
How does high / low clustering ( Getis-ord General G ) work?
The High/Low Clustering (Getis-Ord General G) tool returns four values: Observed General G, Expected General G, z-score, and p-value. The values are written as messages at the bottom of the Geoprocessing pane during tool execution and passed as derived output values for potential use in models or scripts.
How does hot spot analysis ( Getis-ord Gi * ) work?
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 the 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).
How are Moran’s I and Getis related?
Moran’s I can be expressed in terms of the local Gi* values. This is outlined in Section 4 of the 1995 paper of Ord & Getis and it shows that both measures are related to each other. While Moran’s I gives you a more general indication of clustering and repulsion, Gi* is a measure of high/low value concentration. However, both are inherently linked.
When to use Moran’s I or G statistic?
General G statistic can be used to indicate whether high or low values are concentrated over the study area. Hence, if you wish to find out whether your data is clustered in general (auto correlated) use Moran’s I. If you want to know more specifically whether or not there are clusters of high/low values use G stat. There is no way to compare.