Analyzing violent crime, workflow

Workflow using ArcMap

Hinweis:

The steps below are based on 10.3.1 of ArcMap but should work fine for later software releases as well. You may notice slight differences in the maps below if you are using ArcMap 10.5 or later, the result of an improvement in how points are positioned within the fishnet mesh (Optimized Hot Spot Analysis) or within the cube (Create Space Time Cube). You may also notice additional parameters added after the 10.3.1 release for some of the tools used in this workflow. With the release of 10.5.1 of ArcMap, the name of the Create Space Time Cube tool was changed to Create Space Time Cube by Aggregating Points. To complete the workflow steps below, download and unzip the data in the data package provided.

Create a hot spot map of violent crime densities.

1. If you haven't done so already, download and unzip the data package provided at the top of this workflow.
2. Double-click the BrokenBottlesWorkflow.mpk map package to open it.
By default, Geoprocessing tools will run in the background, and any messages output during tool execution will be written to the Results window. When background processing is disabled, however, tool messages are also written to a Progress Dialog box.
3. To ensure you see messages during tool execution, turn off background processing by clicking on the Geoprocessing menu tab and selecting Geoprocessing Options. Uncheck the Enable box for Background Processing.



Tipp:

Whenever possible and appropriate, create your workflow output in a geodatabase rather than as a shapefile. Field names in shapefile output may be truncated, and there are other advantages to using a geodatabase to store your data.
4. Find and open the Optimized Hot Spot Analysis tool. Run the tool using the following parameters. The Analysis Boundary layer defines the study area.
   • Input Features: Violent Crime 2014
   • Output Features: the name of your output feature class such as ViolentCrimeHotSpots
   • Incident Data Aggregation Method: COUNT_INCIDENTS_WITHIN_FISHNET_POLYGONS
   • Bounding Polygons Defining Where Incidents Are Possible: Analysis Boundary



The tool writes a number of important messages to the progress window when it runs (see below) including the cell size it used for aggregation and the distance it used for analysis (the scale of the analysis). Notice that for this analysis the cell size is 1,375 feet and the scale of analysis is 4,563 feet (4554 US Feet for current software). If you are comparing multiple hot spot maps, you will want to make sure that the study area, cell size, and scale of analysis all match.



The output map created by Optimized Hot Spot Analysis is shown below:



2014 violent crime hot spots.

Create a hot spot map of liquor vendor densities to compare to the violent crime hot spot map.

1. Use the Optimized Hot Spot Analysis tool again with the following parameter settings. You will use the output from the violent crime hot spot analysis to define the study area and cell size.
   • Input Features: Liquor Vendors
   • Output Features: the name of your output feature class such as LiquorVendorHotSpots
   • Incident Data Aggregation Method: COUNT_INCIDENTS_WITHIN_AGGREGATION_POLYGONS
   • Polygons For Aggregating Incidents Into Counts: ViolentCrimeHotSpots



Now you can compare the hot spot maps to see where their activity spaces overlap.



Hot spot maps for violent crime and liquor vendors.

Hinweis:

You used the output from the first hot spot analysis as the study area for the second hot spot analysis. The Enrich Layer tool was used to obtain the poverty data for your third hot spot map below; it applied data apportioning to also match the output from the first hot spot analysis. These steps ensure all of the hot spot maps are comparable.

Create a hot spot map of poverty.

While you may use the data enrichment tools either in ArcGIS Online or in ArcGIS Pro to get poverty data, to ensure your results match those below and to avoid consuming credits, use the data provided in the data package you downloaded,Poverty.lpk. The Enrich Layer tool always returns current data.
1. Use Catalog to navigate to the Poverty.lpk layer package you downloaded and drag it onto your map.



The poverty data layer shows the number of households with income below the poverty level.

2. Find and open the Optimized Hot Spot Analysis tool a third time.
3. Set the parameters as follows and run the analysis.
   • Input Features: Poverty
   • Output Features: the name of your output feature class such as PovertyHotSpots
   • Analysis Field: ACSHHBPOV (households with incomes below poverty level)





Hot spot map of the number of households with income below the poverty level.

Overlay the hot spot maps to determine areas of overlap.

1. Find and open the Select Layer By Attribute tool. You will run the tool for all three hot spot maps, each time selecting records where the Gi_Bin field is equal to 3 (a three for this field indicates a statistically significant hot spot at the 99 percent confidence level).



Tipp:

Every time you run a tool, it is recorded in the Results window. Double-clicking on a tool entry in the Results window opens the dialog with parameters filled out. If you need to run a tool several times with slightly different parameters, it is usually quicker to access the tool from the Results window and modify the parameters as needed.

2. Next, find and open the Intersect tool. Add all three layers as Input Features, provide a name for the output, such as iCrimeLiquorPoverty, and run the analysis.



Lizenz:

If you are working with a basic (ArcView) or Standard (ArcEditor) license, first use Intersect for the selected Violent Crime and Liquor cells, then intersect the result with the selected poverty cells.

Clear the selection and turn off layers to see the output. It shows the proposed areas for a liquor moratorium.



Areas where violent crime, liquor vendor, and poverty hot spots overlap.

Create a space-time cube and analyze the crime trends within it.

1. Find and open the Create Space Time Cube tool.
2. For version 10.5 or later of ArcMap, to ensure the cube output aligns with the hot spot output, you must set the Processing Extent to match the Analysis Boundary layer. Click the Environments button at the bottom of the tool UI, expand Processing Extent, and select Analysis Boundary from the Extent drop down.



3. Set the Create Space Time Cube tool parameters as follows and run the analysis. The cube is a netCDF file, so it must be created in a folder rather than inside a file geodatabase. Setting the Distance Interval to match the hot spot map cell size will allow you to overlay the crime trend result with hot spot maps later. For version 10.5 and 10.5.1 only, you must convert 1375 US Feet to 1375 International feet (1375.00275).
   • Input Features: Violent Crime 2014
   • Output Space Time Cube: the name of your output cube such as ViolentCrimeCube.nc
   • Time Field: Date
   • Time Step Interval: 4 Weeks
   • Time Step Alignment: END_TIME
   • Distance Interval: 1375 Feet; for versions 10.5 and 10.5.1 only, use 1375.00275 instead (see note below).



The Create Space Time Cube tool will report that it completed successfully but will not add any new layers to the Table of Contents.

Hinweis:

With versions 10.5 and 10.5.1 of ArcMap, the space time cube will not align with the output from Optimized Hot Spot Analysis unless the cell size is entered in US Feet. US Feet are slightly larger and have more precision that International Feet. With later versions of the software, Feet parameters will automatically be interpreted as US Feet.

Vorsicht:

If you want a valid space-time analysis of incident data (like crime events), make sure each bin in the space-time cube is exactly the same size. Selecting Months for the Time Step Interval, for example, will result in some bins having more days than others (31 days for March, but only 30 days for April). This will bias your analysis because months with more days will likely have more incidents just because they have more days. Use Days, Weeks, or Years instead of Months.

Tipp:

Use the END_TIME option for the Time Step Alignment parameter. If the incident range of dates doesn't divide evenly into your Time Step Intervals, you want the bias to be associated with the first (oldest) time period rather than the last (most recent) time period. For example, suppose your data covers fifteen and a half weeks and you select 1 week for your Time Step Interval. If you select START_TIME instead of END_TIME for the Time Step Alignment, all of the bins will have a full week of data except the bins for the last (most recent) time period; it will only have half a week.

4. Find and open the Emerging Hot Spot Analysis tool.
5. Set the following parameters and run the analysis.
   • Input Space Time Cube: ViolentCrimeCube.nc
   • Output Features: the name of your output feature class such as ViolentCrimeTrends
   • Neighborhood Distance: 0.5 Miles
   • Neighborhood Time Step: 1
   • Polygon Analysis Mask: Analysis Boundary



6. Examine the results. The trend categories are defined as follows.

Pattern Type	Definition
New Hot Spot	A location that is a statistically significant hot spot for the final time step and has never been a statistically significant hot spot before.
Consecutive Hot Spot	A location with a single uninterrupted run of statistically significant hot spot bins in the final time-step intervals. The location has never been a statistically significant hot spot prior to the final hot spot run and less than ninety percent of all bins are statistically significant hot spots.
Intensifying Hot Spot	A location that has been a statistically significant hot spot for ninety percent of the time-step intervals, including the final time step. In addition, the intensity of clustering of high counts in each time step is increasing overall and that increase is statistically significant.
Persistent Hot Spot	A location that has been a statistically significant hot spot for ninety percent of the time-step intervals with no discernible trend indicating an increase or decrease in the intensity of clustering over time.
Diminishing Hot Spot	A location that has been a statistically significant hot spot for ninety percent of the time-step intervals, including the final time step. In addition, the intensity of clustering in each time step is decreasing overall and that decrease is statistically significant.
Sporadic Hot Spot	A location that is an on-again-off-again hot spot. Less than ninety percent of the time-step intervals have been statistically significant hot spots and none of the time-step intervals have been statistically significant cold spots.
Oscillating Hot Spot	A statistically significant hot spot for the final time-step interval that has a history of also being a statistically significant cold spot during a prior time step. Less than ninety percent of the time-step intervals have been statistically significant hot spots.
Historical Hot Spot	The most recent time period is not hot, but at least ninety percent of the time-step intervals have been statistically significant hot spots.
New Cold Spot	A location that is a statistically significant cold spot for the final time step and has never been a statistically significant cold spot before.
Consecutive Cold Spot	A location with a single uninterrupted run of statistically significant cold spot bins in the final time-step intervals. The location has never been a statistically significant cold spot prior to the final cold spot run and less than ninety percent of all bins are statistically significant cold spots.
Intensifying Cold Spot	A location that has been a statistically significant cold spot for ninety percent of the time-step intervals, including the final time step. In addition, the intensity of clustering of low counts in each time step is increasing overall and that increase is statistically significant.
Persistent Cold Spot	A location that has been a statistically significant cold spot for ninety percent of the time-step intervals with no discernible trend, indicating an increase or decrease in the intensity of clustering of counts over time.
Diminishing Cold Spot	A location that has been a statistically significant cold spot for ninety percent of the time-step intervals, including the final time step. In addition, the intensity of clustering of low counts in each time step is decreasing overall and that decrease is statistically significant.
Sporadic Cold Spot	A location that is an on-again-off-again cold spot. Less than ninety percent of the time-step intervals have been statistically significant cold spots and none of the time-step intervals have been statistically significant hot spots.
Oscillating Cold Spot	A statistically significant cold spot for the final time-step interval that has a history of also being a statistically significant hot spot during a prior time step. Less than ninety percent of the time-step intervals have been statistically significant cold spots.
Historical Cold Spot	The most recent time period is not cold, but at least ninety percent of the time-step intervals have been statistically significant cold spots.
No Trend Detected	Does not fall into any of the hot or cold spot patterns defined above.

For this analysis there are no cold spots.

Hinweis:

You can create a 3D visualization of these crime trends using ArcGIS Pro. If you have the 1.1 or a later release of ArcGIS Pro, tools have been added to help you visualize the space-time cube in 3D. With ArcGIS Pro 1.3, a cube explorer add-in is available as well.



Violent crime trends.

Create a hot spot map of unemployment rates.

1. Use Catalog to navigate to the Unemployment.lpk layer package you downloaded and drag it onto the map.



Unemployment rates across the study area.

2. Open the Optimized Hot Spot Analysis tool.
3. Set the parameters as follows and run the analysis.
   • Input Features: Unemployment
   • Output Features: the name of your output feature class such as UnemploymentRateHotSpots
   • Analysis Field: UNEMPRT_CY (unemployment rate)



2015 unemployment rate hot spots.

Overlay the violent crime trend map with the unemployment rate hot spot map to determine areas of overlap.

1. Find and open the Select Layer By Attribute tool. You will use it once to select intensifying, persistent, and consecutive hot spots ("PATTERN" = 'Consecutive Hot Spot' OR "PATTERN" = 'Intensifying Hot Spot' OR "PATTERN" = 'Persistent Hot Spot') and a second time to select the most intense unemployment rate hot spots ("Gi_Bin" = 3).



2. Next, find and open the Intersect tool. Add the violent crime trends and unemployment rate hot spot maps with their active selections, provide a name for the output results such as iCrimeUnemp, and run the analysis.



Clear the selection and turn off layers to see the output.



Areas where unemployment rate hot spots overlap persistent, consecutive, or intensifying violent crime trends.

Finally, select the public high schools within a quarter mile of the overlapping areas.

1. Find and open the Select Layer By Location tool.
2. Set the parameters as follows:
   • Input Feature Layer: Public High Schools
   • Relationship: WITHIN_A_DISTANCE
   • Selecting Features: iCrimeUnemp
   • Search Distance: 0.25 Miles



3. Use the Copy Features tool to copy the selected high schools to a new feature class (this is optional, but it makes mapping and creating reports a bit easier).
   • Input Features: Public High Schools
   • Output Feature Class: the name of your output feature class such as SelectedHighSchools

You are now ready to make your final recommendations.

• Analyzing violent crime - analysis overview
• References and resources for learning more