Spatial Statistics Tools
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| Spatial Statistics Tools |
The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships. While there may be similarities between spatial and nonspatial (traditional) statistics in terms of concepts and objectives, spatial statistics are unique in that they were developed specifically for use with geographic data. Unlike traditional nonspatial statistical methods, they incorporate space (proximity, area, connectivity, and/or other spatial relationships) directly into their mathematics.
Spatial Statistics Tools consist of a set of tools, and it will be explained by explaining all the tools as follows:
Analyzing Patterns Toolset:
Determining geographical patterns is important for understanding how geographical phenomena behave, although you can learn about the general pattern of features and their associated values by assigning them, calculating the statistic determines the pattern. This makes it easy to compare patterns for different distributions or different time periods.
The tools in the Pattern Analysis Toolkit are often a starting point for more in-depth analysis. Using a spatial incremental autocorrelation tool may help you to identify spaces where the processes that enhance spatial aggregation are more obvious, for example,
In determining an appropriate distance (analysis scale) to be used in the examination of hotspots (hot spot analysis). The pattern analysis toolkit is inferential statistics; They start with the null hypothesis that your features, or the values associated with your features, display a spatially random pattern.
Then they calculate the value of p
It represents the probability that the null hypothesis is correct (that the observed pattern is simply one of many possible versions of complete spatial randomness). Probability calculation can be important if you need a high level of confidence in a particular decision. If there are public safety or legal implications associated with your decision, for example,
You may need to justify your decision using statistical evidence. Pattern analysis tools provide statistics that identify broad spatial patterns. These tools answer questions such as, “Are the features in the data set, or the values associated with the features in the data set, spatially clustered?” and “Do the clusters become more or less severe over time?”. The following table lists the available tools and provides a brief description of each:
Average Nearest Neighbor
Calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature.
High/Low Clustering (Getis-Ord General G)
Measures the degree of clustering for either high values or low values using the Getis-Ord General G statistic.
You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box.
Incremental Spatial Autocorrelation
Measures spatial autocorrelation for a series of distances and optionally creates a line graph of those distances and their corresponding z-scores. Z-scores reflect the intensity of spatial clustering, and statistically significant peak z-scores indicate distances where spatial processes promoting clustering are most pronounced. These peak distances are often appropriate values to use for tools with a Distance Band or Distance Radius parameter.
Multi-Distance Spatial Cluster Analysis (Ripleys K Function)
Determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances.
Spatial Autocorrelation (Morans I)
Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic.
You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box.
Mapping Clusters Toolset:
Mapping Clusters perform cluster analysis to locate statistically significant hotspots, cold spots, spatial outliers, and similar features. The Mapping Clusters toolkit is especially useful when action needs to be taken based on the location of one or more groups. One example is the assignment of additional police officers to deal with a range of burglaries. It is also important to locate the spatial agglomerations when looking for possible causes of agglomeration; Where an outbreak occurs can often provide clues as to why it is occurring. Unlike the methods in the Pattern Analysis Toolkit, which answer the question “Are there spatial clusters?” yes or no,
Group mapping tools allow visualization of group locations and extent. These tools answer the questions "Where are the clusters (hot spots/cold spots)?" , “Where are the spatial outliers?” , and “Which traits are most similar:
Cluster and Outlier Analysis (Anselin Local Morans I)
Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
Grouping Analysis
Groups features based on feature attributes and optional spatial or temporal constraints.
The algorithm behind this tool has been enhanced and new functionality has been added to these methods in ArcGIS Pro. To simplify the new features and methods, this tool has been replaced by two new tools. Use the Spatially Constrained Multivariate Clustering tool if you would like to create spatially constrained groups. Use the Multivariate Clustering tool to create groups with no spatial constraints.
Hot Spot Analysis (Getis-Ord Gi*)
Given a set of weighted features, identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.
Optimized Hot Spot Analysis
Given incident points or weighted features (points or polygons), creates a map of statistically significant hot and cold spots using the Getis-Ord Gi* statistic. It evaluates the characteristics of the input feature class to produce optimal results.
Optimized Outlier Analysis
Given incident points or weighted features (points or polygons), creates a map of statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic. It evaluates the characteristics of the input feature class to produce optimal results.
Similarity Search
Identifies which candidate features are most similar or most dissimilar to one or more input features based on feature attributes.
Measuring Geographic Distributions Toolset:
Measuring the distribution of a set of features allows you to calculate the value that represents a characteristic of the distribution, such as center, compactness, or direction. You can use this value to track changes in the distribution over time or to compare distributions of different features.
The Geographical Distribution Measurement Toolkit addresses questions such as:
- Where is the center?
- What is the shape and direction of the data?
- How scattered are the features?
Central Feature
Identifies the most centrally located feature in a point, line, or polygon feature class.
Directional Distribution (Standard Deviational Ellipse)
Creates standard deviational ellipses to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends.
Linear Directional Mean
Identifies the mean direction, length, and geographic center for a set of lines.
Mean Center
Identifies the geographic center (or the center of concentration) for a set of features.
Median Center
Identifies the location that minimizes overall Euclidean distance to the features in a dataset.
Standard Distance
Measures the degree to which features are concentrated or dispersed around the geometric mean center.
Modeling Spatial Relationships Toolset:
Besides analyzing spatial patterns, GIS analysis can be used to examine or identify relationships between features. Spatial relationship modeling tools generate spatial weight matrices or spatial relationship models using regression analyses. Tool array files that generate spatial weights measure how features in a data set are related to each other in space. An array of spatial weights is a representation of the spatial structure of your data: the spatial relationships that exist between features in your data set. Proper spatial statistics incorporate information about spatial and spatial relationships into their mathematics. Some of the tools in the Spatial Statistics Toolbox that accept the spatial weights matrix file are spatial autocorrelation (Global Moran's I), cluster and isolation analysis (Anselin Local Moran's I), and hotspot analysis (Getis-Ord Gi*). The tools provided in the Spatial Statistics Toolbox model relationships between data variables associated with geographic features, allowing you to make predictions for unknown values or to better understand the main factors affecting a variable you are trying to model. Regression methods allow you to check relationships and measure the strength of those relationships. Exploratory regression allows you to quickly examine a large number of Ordinary Least Squares (OLS) models, summarize covariate relationships,
Determine if any set of candidate explanatory variables satisfy all requirements of the OLS method:
Exploratory Regression
The Exploratory Regression tool evaluates all possible combinations of the input candidate explanatory variables, looking for OLS models that best explain the dependent variable within the context of user-specified criteria.
You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box.
Generate Network Spatial Weights
Constructs a spatial weights matrix file (.swm) using a Network dataset, defining feature spatial relationships in terms of the underlying network structure.
Generate Spatial Weights Matrix
Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset.
Geographically Weighted Regression
Performs Geographically Weighted Regression (GWR), a local form of linear regression used to model spatially varying relationships.
An enhanced version of this tool has been added to ArcGIS Pro 2.3. This is the tool documentation for the deprecated tool. It is recommended that you upgrade and use the new Geographically Weighted Regression tool in ArcGIS Pro or later.
Ordinary Least Squares
Performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables.
You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box.
Utilities Toolset:
These helper scripts perform a variety of data transformation tasks. They are designed to be used with other tools in the Spatial Statistics toolbox:
Calculate Distance Band from Neighbor Count
Returns the minimum, the maximum, and the average distance to the specified Nth nearest neighbor (N is an input parameter) for a set of features. Results are written as tool execution messages.
Collect Events
Converts event data, such as crime or disease incidents, to weighted point data.
Convert Spatial Weights Matrix to Table
Converts a binary spatial weights matrix file (.swm) to a table.
Export Feature Attribute to ASCII
Exports feature class coordinates and attribute values to a space, comma, or semicolon-delimited ASCII text file.
- The same topic is available in Arabic from here
Watch this video from the YouTube channel.
In the same way, as described through this site. Watch the video first, then you can search for any tool by writing its name in the search, the language of the video is Arabic, but English subtitles and any language in the world are available. Good luck and God bless you.
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