While crime has largely decreased in many major urban cities in the United States and other countries in the last two decades, some cities continue to face high levels of crime, often leading to economic decline and fear among urban citizens. As with most things these days it seems, there literally is an app for telling us what way home might be safe. Furthermore, research is also beginning to show how complex crime patterns can be.
One recent application looks at open data on crime and combines spatial and temporal information to determine a crime risk index for given areas and times. This index helps determine the likelihood one could be affected by crime using the historical pattern through an entropy weighting procedure. With field experiments, it was shown that the application can help better determine pathways, including routes one would walk between two points, that are likely to be safer and have less risk of a crime incident occurring. The results indicate that an application could then be developed for normal users to download and plan their routes home based on safety concerns.
Why such applications are important does not only have to deal with public safety but also public health. Studies have shown that rates of obesity and generally poor health are often high in areas that have high crime. Spatial context in where healthcare access is available and if high crime is present in these facilities means that people may also lose or have their accessibility to health centers diminished, which can have deleterious consequences for health. Fear of being outside also contributes to higher rates of obesity and poor health in high crime areas.
In recent years, hot spot mapping that collects and uses data to then forecast where crime will likely occur has been a growing trend in policing and crime fighting strategies in many different police departments. However, one recent study looked at integrating such a hot spot approach with studying and determining optimal amount of time police should stay and patrol in areas that are more likely to experience crime. The study found that most officers spend only a few minutes in a high crime area or hot spot, but analysis shows that officers that overlap their patrols with others should spend at least 15 minutes in high crime areas. What this shows is that simply mapping the routes and places where crime is likely to occur also needs to factor in the longevity of the experience a police officer should have in a given space and accounting for the temporal patterns are just as critical in understanding spatial patterns in affecting high crime places. On the other hand, if patrols are too concentrated in one area, then crime is simply pushed to another location, where criminals may adapt to patrols. In such cases, patrolling should be adaptive and also not be easily predictable, balancing between spending too little and too much time in any given potential crime locality.
One issue with crime is that data could be biased in that crime reporting is not even and incidents may occur at higher rates in some areas but are under-reported. Additionally, in building and designing streets and buildings, often safety of access is not fully considered. One recent study used mobile phone data to look at flow patterns of people. This study suggested that buildings and street layouts could better control access to buildings such that it is easier to monitor places using CCTV cameras. While this does raise public and privacy issues, it does demonstrate that CCTV coverage is often not based on the most accurate data and that knowing current and real-time flows of traffic may necessitate the placement of cameras in different regions and changes to camera placement should account for the time of day. Mobile phone data are also a better predictor, in cases, than simply depending on crime data in determining places where public safety might be threatened. 
Spatial analysis has become an important tool in understanding crime patterns. As spatial understanding and GIS are increasingly used, research has proved to show ever more sophisticated patterns of spatio-temporal change that affects when and where crime occurs. Applications have been created to use the latest crime data to tell us where it might be safe to walk, while other research has shown crime patterns shift and strategies need to be adaptive as well as optimal for place and time of police patrols. Using mobile data from phones, rather than only crime data, might be another approach to improve safety, given reporting bias in crime.
 For more on the crime risk estimating index created, see: Xiao, J., & Zhou, X. (2019). Crime Exposure along My Way Home: Estimating Crime Risk along Personal Trajectory by Visual Analytics: Crime Exposure along My Way Home. Geographical Analysis. https://doi.org/10.1111/gean.12187.
 For more on the intersection of health and crime, see: Curtis, A., Curtis, J. W., Porter, L. C., Jefferis, E., & Shook, E. (2016). Context and Spatial Nuance Inside a Neighborhood’s Drug Hotspot: Implications for the Crime–Health Nexus. Annals of the American Association of Geographers, 106(4), 819–836. https://doi.org/10.1080/24694452.2016.1164582.
 For more on the role of time and temporal analysis on patrolling high crime hot spots, see: Oatley, G., S., W., Barnes, G. C., Clare, J., & Chapman, B. (2019). Crime concentration in Perth CBD: a comparison of officer predicted hot spots, data derived hot spots and officer GPS patrol data. Australian Journal of Forensic Sciences, 1–5. https://doi.org/10.1080/00450618.2019.1569141.
 For more on how crime gets pushed to different areas based on patrols, see: Andresen, M. A., & Shen, J.-L. (2019). The Spatial Effect of Police Foot Patrol on Crime Patterns: A Local Analysis. International Journal of Offender Therapy and Comparative Criminology, 0306624X1982858. https://doi.org/10.1177/0306624X19828586.
 For more on the placement of CCTV cameras, see: Lee, J. Y., Kim, K. D., & Kim, K. (2019). A Study on Improving the Location of CCTV Cameras for Crime Prevention through an Analysis of Population Movement Patterns using Mobile Big Data. KSCE Journal of Civil Engineering, 23(1), 376–387. https://doi.org/10.1007/s12205-018-1486-4.