Crime Analysis includes an ArcGIS Pro project with the Crime Analysis Add-In, which includes the Crime Analysis tab and ribbon that can be used to conduct a series of key analytical functions to support crime analysis. Before using the Crime Analysis tools with your data, familiarize yourself with the contents of the Crime Analysis project.

To review the Crime Analysis project, complete the following steps:

1. Browse to the CrimeAnalysis folder and open the CrimeAnalysis.ppkx ArcGIS Pro project.
2. On the ribbon, click the Crime Analysis tab. You will see the ribbon includes five groups of tools:
   • Data Management
   • Selection
   • Tactical and Strategic Analysis
   • Investigative Analysis
   • Create and Share Information
3. On the View tab, in the Windows group, click Catalog, and click Catalog Pane.
4. In the Catalog pane, expand Databases and review the sample data provided.
5. In the Catalog pane, expand Maps and review the sample maps provided.
6. Review the Input Data map. This map contains sample data that can be used as input layers in each Crime Analysis tool to produce a set of output layers. Examples of the output layers are in the resulting maps. Close the map when you are done reviewing it.
7. Double-click on the Enhance Attributes to open the map. This map contains the results of the Enhance Attributes tools including Extract Data Parts to Field and Append Attributes to Polygon. Open the Input Data - 2018 Arrests and Arrests With Enhanced Attributes layers Attribute pane. You'll notice several new fields have been added to the Arrests With Enhanced Attributes, such as weekday, hour, month, day, and year. In addition, the Station Name field has been added by appending the precinct to each arrest record. Close the map when you are done reviewing it.
8. Double-click on the 80-20 Analysis to open the map. This map contains the results of using the 80-20 Analysis tool using the Calls for Service as the input data. In this example, 20% of the calls represent only 2% of the call locations. Open the 2% of Locations- 20% of Calls layer Attribute pane and compare the Cluster Count field to the Percentage field of each call location. Also, notice the Full Address field was preserved from the input data layer. Close the map when you are done reviewing it.
9. Double-click on the Incident Count to open the map. This map contains the results of using the Incident Count tool to count the number of arrests within each police precinct. Open the Arrests by Precinct and Offense layer Attribute pane and observe the number of crimes in each field for each precinct. Close the map when you are done reviewing it.
10. Double-click on the Percentage Change and Select by Date to open the map. This map contains the results of using the Select by Date and Time and Percent Change tools. The Select by Date and Time tool was used to select out violent crimes in quarter 2 and quarter 3, then the Percent Change tool was used to compare the change in these time periods within each precinct. Close the map when you are done reviewing it.
11. Double-click on the Density Change and Select by Date to open the map. This map contains the results of using the Select by Date and Time and Density Change tools. The Select by Date and Time tool was used to select out calls for service that occurred in the daytime and nighttime hours, then the Kernel Density tool was used to create a density raster layer for each time frame. Finally, the Density Change tool was used to compare the change in density raster layers. Close the map when you are done reviewing it.
12. Double-click on the Incident Sequence to open the map. This map contains the results of using the Incident Sequence tool using Report Date as the Sort Field on motor vehicle thefts over a six-month time frame. The tool outputs includes sequence points that are labeled in order of occurrence and sequence lines that connect each point. Close the map when you are done reviewing it.
13. Double-click on the Incident Path (Motor Vehicle to Recovery) to open the map. This map contains the results of using the Incident Path tool on the motor vehicle thefts data. The input origin point is where the theft was reported and the input destination is where the vehicle was recovered. The tool outputs include a path lines layer that connects the origin point to destination using a common identifier. Close the map when you are done reviewing it.
14. Double-click on the Incident Path (Gang Turf to Member Homes) to open the map. This map contains the results of using the Incident Path tool on gang member turf and home locations data. In this example, the input origin is the gang turf polygon and the input destination point is the gang member's home location. The tool outputs include a path lines layer that connects the origin point to destination using a common identifier. Close the map when you are done reviewing it.
15. Double-click on Repeat and Near Repeat Calculator Export Table to open the map. This map contains the results of using Export Near Repeat Calculator Table on burglaries. The Near Repeat Calculator is a free third-party tool created by Jerry Ratcliffe of Temple University that can be used to test for the statistical presence of repeat and near repeat patterns in your data. This tool exports existing point feature layer into the required format in the Near Repeat Calculator. In this example, the burglaries layer is the input layer for the tool, which creates as an output the CSV table in the table of contents. Close the map when you are done reviewing it.
16. Double-click on Repeat and Near Repeat Classification 2D to open the map. This map contains the results of using the Repeat and Near Repeat Classification tool on burglaries using the spatial range of 500 feet and temporal range of 7 days. For each burglary, and for each space and time parameter pair, the tool has classified whether a burglary is the originating incident, a repeat, a near-repeat or not part of a pattern. These classifications are represented with a unique symbol in the output layer. In addition, the tool generates connection lines between each incident in a repeat near repeat pattern, symbolized with graduated width according to the number of days elapsing between the connected incidents. Close the map when you are done reviewing it.
17. Double-click on Repeat and Near Repeat Classification 3D to open the map. This 3D scene contains the results of using the Repeat and Near Repeat Classification tool on burglaries using the same space and time parameters. In this example, the repeat or near repeat patterns of burglaries have been visualized using the time between incidents as a height, or Z value. The tool automatically calculates a Z value field for each feature in the output point layer. The point layer is then visualized in 3D scene by setting the elevation properties of the layer to reflect the Z value field in the layer. The output connection line layer has its 3D height geometry automatically enabled so that it can be immediately used in a 3D Scene. Close the map when you are done reviewing it.
18. Double-click on Repeat and Near Repeat Prediction Zones to open the map. This map contains the results of using the Calculate Prediction Zones tool on burglaries using the spatial range of 500 feet and temporal range of 7 days. The tool generates two outputs, the first is a polygon layer representing risk predictions for future burglaries given the inputs provided to the tool. The polygon layer is symbolized using graduated color, with the darker areas representing areas of highest predicted risk for future burglary victimization. In addition to the polygon output layer, a raster output layer is also generated. This raster can be utilized for visualization purposes or can be incorporated into weighted overlay analyses, such as risk terrain modeling or other predictive models. Close the map when you are done reviewing it.
19. Double-click on Import Cell Sites and Sectors to open the map. This map contains the outputs of the Import Cell Sites tool which was used to import a spreadsheet of cell tower site locations to create site points and polygons representing the sector of each tower where the signal was propagated. The Sites_Phone_1 table contains the geographic data for the cell site point layer in Latitude and Longitude fields, and the data for sectors comes in the form of multiple fields which roughly define the geographic extents of the sectors, using azimuth positioning around the tower, degrees width of each sector beam, and the radial distance of the signal propagating from that sector. Close the map when you are done reviewing it.
20. Double-click on Import Cell Phone Records to open the map. This map contains the outputs of the Import Cell Phone Records tool which was used to import the CDR_Phone_1 table of call records and associate them to a cell site and sector which was previously imported and mapped. Typically, a Call Detail Record (CDR) is obtained by a law enforcement agency through a signed subpoena from a cell service provider. The CDR usually contains the cell site and sector identifier which is used to locate the call record on the map using the cell site and sector data. Close the map when you are done reviewing it.
21. Double-click on Cell Sector Count to open the map. This map contains the outputs of the Dissolve tool which was used to dissolve the cell sector polygons on the unique identifier. The resulting output layer contains the count of calls which connected to each cell sector. Close the map when you are done reviewing it.
22. Double-click on Cell Sector Lines to open the map. This map contains the outputs of the Generate Sector Lines tool which was used to represent the azimuth positioning of the cell tower sector as well as the beam width using lines instead of polygons but does not visualize a definitive extent to the tower’s radial signal strength. Close the map when you are done reviewing it.
23. Double-click on Repeat Call Patterns to open the map. This map contains the outputs of the Repeat Call Patterns tool which was used to identify patterns where two phones are calling each other multiple times from the same locations. This example compares call detail records from Phone A and Phone B, both associated to cell towers. The resulting output layer is a line with a graduated line thickness representing the number of calls between the two phones where each phone was at their given location. The number of calls is also represented with a label. In addition, another output layer in the table of contents contains a line for each unique call, with a directional arrow point from the phone originating the call to the phone receiving the call. Close the map when you are done reviewing it.
24. Double-click on Cell Space Time Comparison to open the map. This map contains the outputs of the Space Time Comparison tool which was used to compare two call detail records that match the same space and time parameters. In this example, the Space Time Comparison has identified has identified one space-time match, where a called occurred on Phone 2 (the secondary phone) within 1000 feet and 30 minutes of a call occurring on Phone 1 (the primary phone). In addition, the tool found a space-only match, where a call occurred on Phone 2 within 1000 feet of Phone 2, but not within the 30 minute window. Close the map when you are done reviewing it.
25. Double-click on Cell Phone Sequence 2D to open the map. This map contains the results of the Incident Sequence tool using the CDR_Phone_1_Sites layer as the source for the input point features parameter, and the Start Time field in that layer as source for the Sort Field parameter. The map contains two output layers from the Incident Sequence tool- a point layer Phone_1_Sequence_points, and a line layer Phone_1_Sequence. Features in the point layer contain information about each call and the associated cell tower where it occurred, and are symbolized by the order in which the calls occurred based upon the field entered into the Sort Field parameter of the tool. The line output layer is a single line feature that connects the point feature in the point layer based on the order defined by the field in the Sort Field parameter. Close the map when you are done reviewing it.
26. Double-click on Cell Phone Sequence 3D to open the map. This scene illustrates how to visualize the movement of a cell phone across space and time. The cell phone records have been associated to CDR_Phone_1_Sites and CDR_Phone_1_Sectors. To create the 3D visualization the Features to 3D by Time tool was used to create the CDR_Phone_1_Sites_3D layer and the CDR_Phone_1_Sectors_3D layer, where the height of the feature is determined by the Start Time field of each call. The height difference between each feature represents the number of seconds between the calls. After the 3D point and polygon layers were created, the Incident Sequence tool was used to generate 3D sequence point and 3D sequence line layers. The Incident Sequence tool automatically generates 3D outputs if the input for the tool is a 3D-enabled layer. Close the map when you are done reviewing it.