1. The Study Area boundary shapefile
Risk terrain modeling can be applied to any geographic extent (i.e. local, regional, global; urban, suburban, rural; land, sea). Obtain a projected polygon shapefile to spatially define this geography. (e.g., the municipality of Springfield). Essentially, shapefiles are your reference layers for orienting and analyzing all other data. Most study area shapefiles are free and readily accessible online. This blog post shows how you can easily get study area shapefiles for anywhere in the United States from the U.S. Census Bureau.
2. Topic/Analysis issue data
Risk Terrain Modeling can be used to analyze almost any topic. The topic data represents the problem or issue that you will analyze (e.g., incident locations of robberies, traffic crashes, or drug overdoses). Obtain datasets for a topic of your choice. Use data that is representative of the entire study area and that contains location information. The data file must be in the format of a shapefile (.shp), KML/KMZ file, or comma delineated (.csv) file with XY coordinates. This blog post hows how to easily geocode your own data tables into KML/KMZ files.
3. Risk factor data
These data represent environmental features of the study area, such as grocery stores, gas stations, or schools. A list of "Risk Factor Suggestions" can be found here. Use data that is representative of the entire study area. All datasets of factors that may spatially connect to the topic/analysis issue should be compiled and inputted for testing with RTM. Do a basic review of published reports and consult professional practitioner experiences to identify possible risk factors and then obtain related datasets. For each risk factor, the data file must be in the format of a shapefile (.shp), KML/KMZ file, or comma delineated (.csv) file with XY coordinates. This blog post shows how to easily get risk factors directly from Google Earth.
The RTMDx software accepts data in three formats:
RTMDx can filter your data by date, time or other attribute values. So, consider including variables in your datasets that will enable you to delve deeper into analyzing spatial risks based on subsets of these data. For example, a burglary dataset may have a variable distinguishing "residential", "commercial" and "motor vehicle" burglaries. Traffic crash datasets may note "pedestrian" incidents or "DWI/DUI" incidents. Robbery incidents that occur at certain "times" of the day or "date" ranges could be analyzed separately from all other incidents. Prepare your data to make it as useful as possible for analyzing in meaningful ways.
Downloading geographic boundary shapefiles from the U.S. Census Bureau is straightforward. Here's how to do it for any location in the United States. This is also a simple way to get "study area" shapefiles for Risk Terrain Modeling (RTM).
1. Open American FactFinder (http://factfinder.census.gov)
2. Click on "Advanced Search" (menu options at the top of the screen)
3. Search for a geography in the "state, county, or place" text box (E.g. Newark city, New Jersey)
4. Click on "Geographies" tab on the left side of the webpage, then click on the "Maps" tab
Video Tutorial for Downloading Boundary Shapefiles
Risk Terrain Modeling (RTM) requires geo-referenced data that are often contained in tables, like spreadsheets or CSV (comma delimited) files. Geocoding is the process of transforming these data tables with descriptions of locations, such as addresses or XY coordinates, to locations on the Earth's surface. Common formats of outputs from the geocoding process are shapefiles (.shp) and KML/KMZ map files that can be viewed in a GIS (Geographic Information System), such as QGIS, or used for spatial risk analysis, such as with the RTMDx software.
There are many batch geocoding services on the internet; some are free and others charge fees. Here we present a readily accessible and free geocoding option. You can easily geocode your data tables, export the feature points as KMZ files, and then use them for Risk Terrain Modeling.
Check this out (7 steps):
1. Open Google Earth Pro on your computer. It's free to download.
2. Click "File" from the menu at the top of the screen, then "Import..."
3. In the dialog box that appears next, select a CSV (comma delimited text) file to import and then click the "Open" button. This will be your data that has addresses or XY coordinates as one of the attributes. Note: Your data should contain street address, city, and state as their own columns. If you have XY coordinates, they should be in two separate columns.
4. Follow the on-screen instructions in the "Data Import Wizard". Click the "Next" button to proceed through the steps. Be sure to set the "Street field", "City field", and "State field" accordingly.
5. After you click the "Finish" button in the Data Import Wizard (previous step), you may see a notification that the batch geocoding is in progress. Let it work. If addresses could not be geocoded, a dialog box will appear allowing you to review and repair the addresses.
6. When geocoding is complete, you'll be asked if you want to "apply a style template to the features you ingested?" This lets you adjust symbology, colors, etc. It's optional, and up to you. For this example, I clicked "No".
7. Now you should see a points on the screen and a map layer appear in the table of contents under "Temporary Places". That's your map with geocoded data. You can zoom in/out, etc. You can right-click on the map layer and select "Save Place As..." to export a KMZ file. Now you have the map on your computer that you can share or upload to the RTMDx software to use for Risk Terrain Modeling.
You may also want to try geocoding in QGIS, a free and open source Geographic Information System. Here's how: www.gislounge.com/how-to-geocode-addresses-using-qgis
Risk Terrain Modeling (RTM) analyzes crime patterns to identifies features in the environment that attract criminals and enable their illegal behavior. (RTM is also used for other types of events and human behaviors, but that's a discussion for another blog post). The RTM diagnosis makes very accurate forecasts of places where resources get deployed in order to intervene -- to reduce risk and prevent crime.
Many physical features make up a landscape, and the way they co-locate or interact in space can influence behaviors, events and outcomes. Here is a starter list of environmental features that could be considered for testing with RTM, such as fast food takeout, convenience stores, rooming houses, coffee shops, schools, alleyways, and so forth. There are many sources to get these datasets, including your own/agency CAD/RMS systems, open data portals, local government records, or business directories. We want to share another option: Google Earth Pro.
In Google Earth, you can search for place features and then export the feature points as KMZ files. Then you can easily use the KMZ files for Risk Terrain Modeling in the RTMDx software.
Check this out (5 steps):
1. Search for the places (i.e., risk factors) of interest. E.g. "Coffee Shops in Newark"
2. A list appears and the points appear on the map.
3. Click the small folder icon to "Copy the current search results to My Places"
4. In Google Earth, with the search results map layer in your My Places table of contents, simply right-click on the layer and select "Save Place As..."
5. Open the RTMDx software and click the "Upload Files" button to add your newly acquired risk factors in KMZ format. Then run your RTM analysis.
We’re frequently asked how to get started with Risk Terrain Modeling and the RTMDx software. Here’s an overview:
The RTMDx software is delivered to you through secure login, allowing easy access by all of your project partners and stakeholders while you remain in full control of your data. It's easy to use and priced under $5,000 (that's it! ...no other costs or fees). Rutgers University developed this evidence-based software with support from the U.S. Department of Justice, National Institute of Justice. Check out the click-through tour for an overview of the software's interface. Contact the Rutgers Center on Public Security for questions or to schedule a live demo: email@example.com
With RTMDx, Risk Terrain Modeling is straightforward. Deliver actionable information quickly and easily. To see some of the ways it has been used to support police patrols, investigations and crime forecasting, check out this YouTube video.
Crime prevention and risk reduction initiatives meet your local needs and expectations. The most common form of implementation follows 3 basic steps. This Quick Start Guide (PDF) lays them out simply.
Once you’re comfortable with the basics, you can build on them to expand risk-based policing operations and improve coordination among other stakeholders. RTMDx software serves as more than just an analytical tool for Risk Terrain Modeling (RTM). Reports and maps help to focus resources at high-risk places. Here are 4 short case studies as examples. Here's the annual report from the risk-based policing initiative in Atlantic City (with crime drops over 36%).
RTM with RTMDx keeps problem-solving efforts grounded, and evidence-based. It adds context to ‘big data’. It informs decisions for deploying resources and directing strategies efficiently. Here are some of the many other benefits.
Click here to get started with RTM.
RTM Meets All of These Requirements
1. Data used in the analysis should be reliable and valid (i.e., content and construct validity). The data sets and their sources should allow for replication and continued forecasting. This includes the requirement that the forecast technique does not rely heavily on the outcome of interest (i.e., the dependent variable) to be the predictor (i.e., independent variable). Such a forecasting technique would not be sustainable if it were both actionable and successful since outcomes would ultimately be prevented.
2. The outputs of the forecast should be operational. It should be reasonably clear what to do with the information to respond to the forecasted effects. Knowing where to go is a start. But forecast outputs should also inform decisions about what to do when you get there.
3. The method of the forecast should be operationalized consistently from one instance to the next. Data sets and sources may change, as will analysts, etc., but the reliability of the forecasting method must withstand multiple iterations, in different settings, by different people, and for different types of outcome events.
4. The elements of the forecast should be articulable, with the importance of each factor relative to one another directly measured. The direct impact of key factors on outcomes should be demonstrable (i.e., internal validity).
5. The output results should be within a range of reasonable expectations (i.e., face validity). The forecasting process should be able to justify why a result was produced or else the forecasting process should be able to be revised in a non-arbitrary way.
6. The method of the forecast should be able to tolerate the products of successful interventions. Especially those that are based on the intel from prior forecast iterations. Basically, any successful forecast should yield valuable information that can be operationalized for preventative action. However, if the preventive action is successful at reducing crime incident counts and/or changing their spatial patterns, then it could directly affect the precision/accuracy of future forecast iterations. This is why a successful forecasting technique should be able to tolerate the products of successful interventions.
Police agencies live under the constant threat of being too successful. This statement is a bit pretentious and a lofty goal that is difficult to achieve. But let’s face it; crime problems fuel police agency budgets. When crime problems emerge and can be articulated, police executives demand more resources to respond. When police achieve measured and sustained crime reductions, it becomes more difficult for departments to justify the need for more officers, technology, or other resources.
Police agencies live under the constant threat of being too successful.
Successful policing is often taken for granted when crime counts are low – even if police activities were initially credited with reducing crime. Unfortunately, a disconnect between inputs and outcomes of policing occurs when measures of police productivity are reliant on the persistence of the crime incidents that police departments aim to prevent. Such is the case when productivity measures depend on people to be stopped, ticketed or arrested for crimes already committed rather than on more sustainable and benign measures, such as police officers’ efforts associated with reducing crime risks, or other actions taken to mitigate spatial or situational attractors of illegal behavior at risky places.
Police actions have an important role to play in affecting crime risks. They can deter offenders, embolden victims, and assist in the hardening of targets. These products can have the overall impact of reducing crime occurrence. But we must separate what we would see as crime prevention and response from risk reduction strategies. A risk reduction strategy requires that police identify the environmental conditions in which crime is likely to appear. This diagnosis can be ascertained from existing free resources, such as risk terrain modeling. Police can then propose strategies to address these conditions and interrupt the interactions that lead to illegal behavior settings and new crimes. Alternatively, police dealings with people at crime hot spots may have the effect of deterring criminals or even reducing crime counts at these areas in the short-term. But, despite this, the underlying spatial factors that attract and generate problems in these areas do not go away. So, three things can happen: crime disappears, it displaces, or it subsides to reemerge later. This is “good” for justifying police budgets, but it's a burden on police departments in pursuit of the mission to prevent crime with sustained oomph.
Sparrow (2015) wrote a provocative article about measuring performance in a modern police organization. He argues that reported crime rates will always be important indicators for police departments. However, substantial and recurrent reductions in crime figures are only possible when crime problems have first grown out of control. A sole reliance on the metric of crime reduction, Sparrow explained (p. 5), would “utterly fail” to reflect the very best performance in crime control practices when police actions are successful at keeping crime rates low and nipping emerging crime problems as they bud. Beyond looking at crime rate changes, a risk reduction approach to solving crime problems suggests success from interventions when factors other than merely crime counts improve. Risk-based policing has dual objectives: One to reduce crime counts and the other to reduce the influences of known risk factors. Risk-based policing allows police leaders to demonstrate to elected officials and budget-makers how what police are doing works to reduce crime and why the crime rates are lower because of their efforts.
While risk-based policing can accommodate the ideas of situational crime prevention in targeting certain locations for intervention, the ideals extend beyond a focus on opportunities for crime or the “crime triangle”, and, instead, target all aspects of the context that raises the risk that crime will occur. This opens the door to a broad array of police productivity and success measures that do not demand the illegal behaviors to continue, or crime incidents to be reported, in order for police to validate or justify their own value and continued budget-item existence.
Risk terrain modeling, for instance, provides an approach to understanding crime occurrence by identifying the relative influences of factors that contribute to it; risk terrain maps inform decisions about which places can be targeted to reduce these risks. This is inherent in risk-based policing, which considers the effects of guardianship, victim characteristics, locations, precipitators, exposures, and offenders in a risk narrative that is contextually dynamic. Iterative risk terrain models and reconsiderations of risk narratives make risk reduction activities transparent, measurable and testable. In this way, risk-based policing enables police departments to be appropriately credited with success and judged against the probable consequences of alternative or non-existent engagements in the communities they serve and protect.
risk-based policing enables police departments to be appropriately credited with success and judged against the probable consequences of alternative or non-existent engagements in the communities they serve and protect.
Cultural shifts within police departments away from ‘crime fighting’ and toward ‘risk management’ costs very little financially. Coupled with sustainable investments in human capital, smart data, continuing education, and current technology, risk-based policing can go a long way to help agencies fight and prevent crime, and then to credit their actions with success in a way that is obviously clear and sustainable. Recent uses of risk terrain modeling in various practical settings suggest that police departments are able to incorporate risk management into their analytical and cultural frameworks with real success. More departments should follow their lead, and city councils should support their efforts.
Grubesic and Mack (2008) argued that we cannot treat space and time as independent entities, but, rather, as interdependent ones that interact to create situational risks. The interactions among people and their geographies are deeply fluid in the sense that no feature retains its “social relevancy” permanently (Kinney, 2010, p. 485). Places can be “fantastically dynamic” (Jacobs, 1961/1992; p. 14). Basically, places can have different risks at different times because a criminogenic feature of the landscape can have varying spatial influences depending on its social relevancy at different times and under particular circumstances (e.g. Gaziarifoglu, Kennedy, & Caplan, 2012; Irvin-Erickson, 2014; Yerxa, 2013).
The RTM approach allows not only an assessment of risk factors at certain places, but an ability to judge their effects at different times. To do this with the RTMDx Utility, simply prepare your data by first (1) isolating crime incidents (i.e. the events you wish to study) that occurred within the time period(s) of interest to you. Then (2) produce risk terrain models for each of the temporally different datasets. For example: create three shapefiles of robberies that occurred between 7pm-3am, 3am-11am, or 11am-7pm, respectively. Then, use these three datasets to run three different risk terrain models. Something similar can be done to study risky places for crimes occurring on the weekend vs. weekdays; daytime vs. nighttime hours; school year vs. summer months; sporting (home) game days vs. (away) game days; etc.
* Glossary (see the full RTM glossary, here)
For references, see Risk Terrain Modeling: Crime Prediction and Risk Reduction (2016; UCPress), by Caplan & Kennedy.
Criminologists have long sought to explain why crime occurs at certain places and times. These inquiries have led to observations and documentation of many different factors that contribute to the spatial and temporal dynamics of illegal behavior and crime victimization. Victims and offenders have been considered in various contexts, formed by both the activities that individuals pursue but also by the nature of the environments they occupy. Some places, we know, are more likely to be locations of crime than others. That is, where exposure to crime events is relatively high. This may be the case because of the characteristics of people who frequent these places or it may have something to do with the qualities of the environments themselves. Of course, both of these can vary by time of day, week, or year. If we concentrate on the characteristics of places, we can focus on factors that are conducive to crime occurrence, offering a means by which to target certain places that are more likely to promote illegal behavior.
Multiple approaches to studying spatial crime risks have merit, and have helped create a more complete picture of the underlying processes that contribute to crime occurence. Analytical models are tools for the acquisition of knowledge. "Knowledge," said Daryl Morey, the man hired to empirically minimize uncertaintly in basketball, "is anything that increases your ability to predict the outcome" . Predictive crime models allow criminologists to explore attributes that lead to crime outcomes, and to determine how much weight to give to each. Having prediction models without any human opinion can force people to ask the right questions. Models may not be the "right" answer so much as the "better" answer, and, thus, can lead to better and more insightful questions.
Crime prediction needs more than big or little data. It needs people and experts. Crime prediction models must, at some point, invite human judgement into decision-making processes. The task of the criminologist is not simply to explain empirical results via theory or opinion, but to provide actionable prescriptions for how to use knowledge to combat crime and its consequences. The task of the criminologist is to facilitate and moderate human judgements into decision-making.
The talent of a skilled criminologist is to listen to both empirical evidence and subjective human judgement, and to blend the two. A person's understanding of what is seen or heard changes with the context in which each is aquired. The criminologist's mind needs to be in a constant state of defense against all the junk that's trying to mislead it, and to relate context to data without being overwhelmed by the context of data. The criminologist's task is to communicate meaning out of the signals and noise, and to soundly advise how to monitor connections to crime, how to assess spatial vulnerabilities, and how to act in order to reduce the worst effects of their predictions.
Post inspired by Risk Terrain Modeling: Crime Prediction and Risk Reduction (2016; UCPress), by Caplan & Kennedy and by "The Undoing Project" by Michael Lewis. Endnote from pg. 31 of "The Undoing Project" by Michael Lewis.
In our various collaborations with researchers and practitioners throughout the world, we learned… realized… from multi-city projects that the spatial dynamics of crime are not the same in different settings, even for similar crime types. Standard patterns of crime cannot be expected across study settings.
Think about this through the analogy of a kaleidoscope. The kaleidoscope itself represents the particular environment, or study setting, that we are interested in examining (see Figure). The pieces of the kaleidoscope (i.e. the glass and the cylinder) are similar from one time to another. The mechanisms for bringing the pieces together in certain patterns (e.g. gravity, the roundness of the cylinder) operate constantly, and the characteristics of the pieces (color, value) are the same from one turn to another. The patterns that are formed, however, change with different combinations of the pieces. So, it is with crime locations that the shards of glass represent features of that environment, such as bars, fast food restaurants, grocery stores, etc. that could attract illegal behavior and create spatial vulnerabilities. Moving from study setting to study setting represents a turn of the kaleidoscope whereby the pieces come together in different ways, creating unique spatial and situational contexts that have implications for behavior at those places.
Figure: The crime risk kaleidoscope illustrates how unique settings for illegal behavior form within and/or across jurisdictions as pieces come together in different ways, creating unique spatial and situational contexts for crime, as depicted by the triangular or hexagonal outlines in the figure.
When we diagnose the underlying characteristics of “hot spot” areas across jurisdictions, we realize that the characteristics of places where these crime incidents are occurring in each city are very different. As evidenced by this study, detailed in the forthcoming book, Risk Terrain Modeling: Crime Prediction and Risk Reduction (2016; Univ. of Calif. Press), even though crime problems can cluster within cities, the ways in which features of a landscape come together to create unique behavior settings for crime is not necessarily generalizable across cities.
Mindful of the kaleidoscope metaphor, it is not safe to assume that a “standard” response to crime problems will provide similar returns across all environments. This is true for areas within jurisdictions and also across jurisdictions. So one crime problem, such as robberies, will not necessarily respond to a “1-size-fits-all” intervention strategy (even if the strategy worked elsewhere). Behavior settings differ, so interventions need to be tailored accordingly. Risk terrain modeling facilitates this custom analysis of crime problems at various geographic extents.