Increasingly public data on crime statistics that are spatially referenced has allowed a better understanding of the geography of different crimes. This ranges from serious to minor incidents, including vandalism. Vandalism is the act of deliberately destroying or damaging property. It is not usually considered a serious crime, but it can have major effects on communities by negatively impacting perceptions of communities and economic assets.
In a relatively early paper on this topic, the presence of collective resources, such as public property in parks or similar type of common resources, appeared to be one key factor in affecting where vandalism had relatively high rates. This was shown in the area of Malmo, in Sweden, using spatial regression models on available crime data. Public resources can act as an attractor but also potentially some areas, such as parks, could easily be used to hide destruction of property or act as areas where individuals may gather and are likely to conduct acts of vandalism. Where studies have looked at geographic and other factors, contextual variables, including what is present in a given place such as public or even some private resources, socio-economic status, cultural status, and environmental factors all can be shown to have a relationship to vandalism. This largely reflects what earlier works have demonstrated regarding vandalism. While vandalism is often seen as a type of nuisance crime, it can also have more pronounced economic damage in cases where major resources, such as oil pipelines, are damaged. For some countries, losses can be in the millions and could serious affect economic development without adequate protection.
There are geographic tools to track crime and vandalism, such as Esri’s crime map for cities. These allow one to see what type of crimes around a neighborhood have been reported, with vandalism being one type.
In fact, Esri has recently been developing its public crime analysis tool integrated within ArcGIS Pro that is intended for urban and city managers or even police. In this case, not only can crime be visualized in the tool, but analysis can include forecasting likelihood of given crimes including vandalism in neighborhoods. Data outputs include summary statistics and ability to visualize over given time intervals the location of acts of crime.
Other projects, including using Google Street View, have developed and utilized different vandalism analytical tools for detecting likely patterns of vandalism within areas. Volunteer Geographic Information (VGI) in this case can be used to record where events occurred, where public information fed into a geographic framework could be used as part of forecasting tools; however, these can be deficient given potential biases in reporting. Other forecasting methods, using stochastic Bayesian modeling under different spatio-temporal possibilities, could be one way to better estimate likely areas where crime occurs. Using a combination of Twitter, Foursquare, and spatial context information from Google Street View enabled one effort to visualize reported crime and develop pattern analysis of where given crimes are likely to occur based on more context-driven information provided by descriptions. The results of this work showed that contextual information, which is not limited to demographic data of who perpetrated given vandalism, from reporters of crime is a stronger predictor in predicting where vandalism is likely to occur than even the more common or traditionally used data used by police.
Vandalism is usually not considered a major crime, but its presence can have detrimental economic and mental effects on populations. Our understanding of vandalism has occurred through approaches looking at patterns of where vandalism events occur, while more recent works integrate social media as well as GIS data to understand current and potentially future patterning of vandalism. Spatial tools are also now created to enable cities and other public entities to more easily monitor vandalism while providing some analytical capability in trying to understand key factors that cause vandalism or its likely presence in places.
 For more on the relationship between public resources and vandalism, see: Ceccato, V., Haining, R., 2005. Assessing the Geography of Vandalism: Evidence from a Swedish City. Urban Studies 42, 1637–1656. https://doi.org/10.1080/00420980500185645.
 For more on socio-economic factors in vandalism, see: Bhati, A., Pearce, P., 2016. Vandalism and tourism settings: An integrative review. Tourism Management 57, 91–105. https://doi.org/10.1016/j.tourman.2016.05.005.
 For more on how vandalism can cause major economic damage, see: Udofia, O.O., Joel, O.F., 2012. Pipeline Vandalism in Nigeria: Recommended Best Practice of Checking the Menace, in: Nigeria Annual International Conference and Exhibition. Presented at the Nigeria Annual International Conference and Exhibition, Society of Petroleum Engineers, Lagos, Nigeria. https://doi.org/10.2118/162980-MS.
 For an example map and viewer of crime data happening around neighborhoods, see: https://www.arcgis.com/apps/webappviewer/index.html?id=1bfbbacd381348b29c4e9f02375e0a16.
 For more on the ArcGIS Pro tool for crime analysis, see: https://www.esri.com/arcgis-blog/products/arcgis-pro/public-safety/introducing-the-new-crime-analysis-tools-in-arcgis-pro/
 For more on vandalism forecasting methods using web-based data, see: Zhang, Y., Siriaraya, P., Kawai, Y., Jatowt, A., 2019. Analysis of street crime predictors in web open data. J Intell Inf Syst. https://doi.org/10.1007/s10844-019-00587-4.
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