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A Normal Q-Q Plot is used to assess the similarity of the distribution of a set of values to that of a standard normal distribution the typical bell curve, when shown on a histogram. The line on the Normal Q-Q plot shows expected values for a normal distribution—the closer the values to the line, the closer the distribution is to normal. In this example, the concentration of the elements Phosphorous for a set of soil samples is close to normally distributed.

The spatial relationship is usually defined as distance how far apart features are but can also be other forms of interaction between features.

In the example below, the output of the Standard Distance tool displayed graphically as a circle is calculated using the distance of each wildlife sighting from the calculated center of the sightings. Some spatial statistics consider both the spatial relationships of features and the values of an attribute associated with the features. These are known as weighted statistics—the spatial relationship is influenced by the values. Weighted spatial statistics are used to find out if features having similar values occur together—if, for example, schools with similarly high or low test scores form clusters.

In the example below, the center of parks is weighted by the number of visitors at each park represented by the size of the green circles. Descriptive statistics are often useful for comparing two sets of features for the same area. The example below compares the distribution of senior citizens top to that of children under 5 bottom for the same set of census tracts.

In the example below, the standard distance circles for the American Indian and African American population show that the distribution of the African American population in this area is much more compact. Inferential statistics use probability theory to either predict the likely occurrence of values using a set of known values , or to assess the likelihood that any pattern or trend you see in the data is not due to chance.

The function provides a measure of the pattern or relationship. You then perform a statistical test on this measure to determine whether it is significant at some level of confidence. In the example below, the map on the left shows clusters of census tracts having a high number of senior citizens orange or a low number blue , at a 90 percent level of probability; the right map shows clusters at a 99 percent level of probability.

Spatial Analyst and Geostatistical Analyst. A core set of descriptive statistics that summarize the values for a single field is available from several locations in ArcGIS for Desktop —the table window in ArcMap, the table preview tab in ArcCatalog, and the Statistics toolset within the Analysis toolbox.

ArcMap table window or ArcCatalog table preview tab. To summarize a field by one or more other fields for example, to count the number of parcels in each land-use class, sum the area in each land-use class, or find the average parcel size in each class , use the Summarize option on the ArcMap table window or the Frequency tool in the Statistics toolset in the Analysis toolbox.

The Spatial Statistics toolbox contains a number of statistical routines for analyzing the distribution of a set of features, analyzing patterns, and identifying clusters. The Spatial Analyst includes several statistical functions that can be used to analyze rasters, primarily to summarize attribute values and assign the summary statistics to cells in a new raster layer.

These are located in several different toolsets with the Spatial Analyst toolbox. Calculates the specified statistic for each cell based on multiple inputs. Summarizes the values for a raster within a defined neighborhood around each cell and assigns the value to that cell in the output raster. Summarizes values for point feature attributes within a defined neighborhood and assigns values to cells in the output raster.

Summarizes values for line feature attributes within a defined neighborhood and assigns values to cells in the output raster. Summarizes values of a raster surface by categories or classes zones of the input raster or polygon dataset.

The Geostatistical Analyst —while focusing on the creation of surface from a set of sample points—also contains a set of tools for visual exploration of data values using charts and graphs. These are often used prior to surface creation to decide which parameters to use for a specific set of data but can also be used generally to explore your dataset.

The tools allow you to explore the distribution of values, whether there is a directional trend in the data, and whether there are relationships between two attributes for example, to see if the values vary together or inversely.

The tools are available from the Explore Data option on the Geostatistical Analyst toolbar. Arc GIS for Desktop. The goal of statistical analysis is to identify trends. Please check the box if you want to proceed. The Whistleblower Protection Act of is a law that protects federal government employees in the United States from A smart contract, also known as a cryptocontract, is a computer program that directly controls the transfer of digital currencies A risk map, also known as a risk heat map, is a data visualization tool for communicating specific risks an organization faces.

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Statistical analysis helps you extract additional information from your GIS data that might not be obvious simply by looking at a map. Simple Statistical Analysis See also: Designing Research. Once you have collected quantitative data, you will have a lot of numbers. It’s now time to carry out some statistical analysis to make sense of, and draw some inferences from, your data. There is a wide range of possible techniques that you can use.