Incorporating Time into RTM

While it's true that vulnerability to crime varies across space, it is also true that the same places can have varying risk levels depending on the time of day, week or year, despite the stationary nature of a landscape’s physical features. This is because spatial influences* of environmental features are dynamic and can change. For instance, the spatial influence of a bar at 10pm on a Friday is different than its spatial influence at 10am on a Tuesday.

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.

As shown by the sample map and table, to the right, spatial risks may cluster differently across police shifts. Environmental risk factors (and related event contexts*) can also be unique to the week days and weekend days.

A review of temporal heat maps, such as the one to the left, can inform the production of temporally constrained risk terrain models that isolate the risk factors attracting criminal behavior at peak days or time periods. (Learn how to create heat maps in Microsoft Excel, here). For instance, risk terrain models can be produced for the crime-dense periods of “Monday thru Thursday, from 6-10pm” or “Friday thru Sunday, 5pm to midnight.” This spatial-temporal intelligence fuels stakeholder discussions about the event contexts of crimes nearby to key risk factors to make connections that advance the risk narrative*.

Studying spatial-temporal crime risks with RTM is technically straightforward. Be sure that the time periods you divide your data into are meaningful and considerate of how the information communicated by the risk terrain models and maps will be used for decision-making.

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* Glossary (see the full RTM glossary, here)

• Spatial Influence - The way in which features of an environment affect behaviors at or around the features themselves; The measurable link between features of a landscape and their impacts on people and the ways in which they use space; The articulation of perceptual cues observed about features and gleaned from personal opinions, experiences, and empirical knowledge about similar features or characteristics thereof under other similar circumstances.
• Event Context - A situational-based understanding of crime incidents that looks at behavioral outcomes as less deterministic and more a function of a dynamic interaction among people that occurs at places.
  
• Risk Narrative -- A spoken or written account of connected events. It is a story, so to speak, about how events, such as crimes, relate to other phenomena in their environments.

For references, see Risk Terrain Modeling: Crime Prediction and Risk Reduction (2016; UCPress), by Caplan & Kennedy.

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