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How do police know it’s what they did that made the difference? This is a great question, and frequently asked by crime analysts learning about Risk Terrain Modeling (RTM). One way to determine the impact of law enforcement and policing actions at high-risk places is to decide on the response strategy in advance of implementation, intentionally focusing policing actions on one (or more) of the risk factors. Specifically state this intent from the get-go. The factors that could be focused on will be identified by the risk terrain model. Then compare new risk terrain models to the older ones. For example, if "convenience stores" were a top risk factor for motor vehicle thefts, a risk reduction strategy might be to increase patrols around convenience stores located in high-risk areas; to put sign-in sheets in the stores for officers to engage more with the managers while showing a police presence; or to display informational posters on store windows reminding customers: "Turn off car. Take keys. Lock doors. Prevent theft." There are more and probably better examples than this. But hopefully you get the idea. Then, after deploying that risk reduction strategy for a period of time (e.g., 1 month), run a new risk terrain model and compare the Relative Risk Value (RRV) of the before-and-after results for the targeted risk factor. If the factor that you intentionally focused on has a lower RRV after the intervention compared to what it had before your intervention activities, then you may have had an effect. If crime also went down during the intervention time period, then you may have prevented crimes by mitigating the risk factor. Both of these things -- risk reduction and crime prevention -- could be credited to the police activities. If the risk factor (e.g., convenience stores) was present in the risk terrain model before the intervention and wasn't present in the model after the intervention, then that could mean you completely mitigated the risk factor's spatial influence based on your actions. It's no longer a crime attractor/generator. Now you can deal with any new crime dynamics that exist, which you know well based on the newly updated risk terrain model that serves as your next month's forecast and intel for risk reduction strategies and deployments. 
Develop Spatial Risk Narratives. A risk narrative is a spoken or written account of how events, such as crimes, relate to other phenomena in the jurisdiction. Crime events occur within spatial and situational contexts. So, assessing the crime risk narrative is an endeavor for police and other stakeholders to think spatially about criminal events and behaviors.
In Atlantic City, NJ, risk narratives were formed from the results of Risk Terrain Modeling (RTM): police and other stakeholders believed shootings to be connected to drug sales and related turf conflicts whereby ‘convenience stores’ are the places where drug buyers are solicited; Nearby ‘laundromats’ are locations where drug transactions are made; ‘vacant buildings’ located nearby are used by drug dealers as stash houses for drugs and weapons, or by drug buyers to use drugs after purchase. These three environmental features were top risk factors identified by RTM. [Atlantic City is discussed in the forthcoming book "Risk-Based Policing: Evidence-Based Crime Prevention with Big Data and Spatial Analytics" (2018, Univ. of CA Press) and was spotlighted on the National Geographic Channel in 2017.] When community stakeholders used the data driven evidence to surmise that drugs, prostitution, retail businesses, and vacant properties are related in this way to shootings, they were more likely to agree with police that certain places will probably experience shooting incidents in the future. This led to conversations about how to effectively target and remediate the problems in these locations. That is, to disrupt the narrative. Policing interventions reduced shooting events by focusing preemptively on the risky places. Areas around laundromats received directed police patrols; police officer ‘meet-and-greets’ with convenience store managers were implemented at frequent intervals each day; and the city’s Planning and Development department prioritized remediation of vacant properties and installations of new LED street lights (to replace dimmer halogen lamps) at the highest risk places. Risk narratives aid police in articulating crime problems in diverse ways, beyond those entailed in established paradigms, practices, or procedures. Risk narratives support reasoning with hypotheses, whereby preconceived notions about a crime problem and its relationships to space and time are tested and addressed accordingly. Risk terrain modeling informs and advances risk narratives. Risk narratives enable effective risk governance led by police and supported by other city officials. Crime is dynamic, and a function of the interaction of people at places. You may already know where crimes are happening in your city. RTM helps to identify why these places are chronic problems. You bring meaning and context to the analytical results via risk narratives. This allows policing operations to be enhanced, not replaced, by technology. From Risk Terrain Modeling and risk narratives about spatial and situational contexts of crime, strategies for police are developed that focus on risk reduction in order to prevent crimes.  According to the Theory of Risky Places (TRP), risk levels of crime could and should be computed at places according to the interaction effects of known locational features. Risky places are particular portions of space that have been assessed for their likelihood of experiencing crimes and to which a relative risk score has been attributed. According to the TRP, risky places are a product of the combined effects of vulnerabilities and exposures to crime.
Vulnerability comes from the presence of a combination of spatial influences of features of a landscape that enhance the likelihood of crime. This is articulated through Risk Terrain Modeling (RTM). Exposures refer to the historical facts and collective memories people have about places and the events that occurred there, such as knowledge about crime hot spots. A vulnerability-exposure analytical framework considers the integration of RTM and measures of exposures to crime, such as kernel density estimated hot spots. This provides a basis for analyzing the system processes of spatial influence whereby crimes emerge, persist or disappear. The combined effects of vulnerability and exposure lead to the identification of risky places. For more on this, see Chapter 5 in "Risk Terrain Modeling: Crime Prediction and Risk Reduction" by Caplan & Kennedy (2016; Univ. of CA Press).
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 ShapefilesRisk 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: info@rutgerscps.org 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 Requirements1. 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.
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riskterrainmodeling.com is maintained by the Rutgers Center on Public Security (RCPS). It is the official website of Risk Terrain Modeling (RTM) research and resources, based out of Rutgers, The State University of New Jersey.
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