Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.

Today, major planning agendas in Europe steer the future of territorial organization towards an urban polycentric perspective. The accessibility to services of general interest (SGI), a significant source of spatial inequality in Europe, is one of the key challenges to address. However, instruments needed to support the implementation and monitoring of territorial policy measures regarding the distribution of, and accessibility to SGI are still under-developed. Studies generally relate to the (intra)regional and not the national scale. When they do so, they lose local specificity as they often consider the existence/availability of services at a given scale and not the actual capacity to reach their (often just estimated) location through the transport network. In the context of the revision of Portugal’s National Plan for Territorial Planning Policies, this paper presents a comprehensive multi-criteria location-based approach for measuring the factual accessibility to a representative range of SGI at the national Portuguese scale. Results are evaluated considering the dichotomy between centrality and periphery, high and low density, and the regional disparities found. High accessibility values do not necessarily mean greater territorial cohesion. Contributions to the development of national planning policies that respond to cohesion challenges are also debated.

Although CPTED is recognized as an important crime deterrent for some decades, there are countries where it is still in its infancy, and knowledge and dissemination are scarce. Portugal is such an example. Only in the last decade has research specifically focused on CPTED, with little convergence between the different spheres of knowledge production, such as the academia, the police or the administration. In this paper, we argue that to build a CPTED culture, these spheres need to converge. Using the Portuguese CPTED experience as a case study, we apply methodologies of social network analysis to explore the relationships between different actors and the research time-line. Through a bibliographical analysis, we further delve on the authors and theories that most influenced Portuguese CPTED. This dual approach allows introducing the little known Portuguese CPTED experience to the international community, as well as discussing how the mechanisms of knowledge production integrate towards a more cohesive implementation.

The article “Building a CPTED research culture in Portugal: a bibliometric and social network analysis”, written by Miguel Saraiva, Ana Amante, Hélder Santos, Paula Ribeiro, was originally published electronically on the publisher’s internet portal on 29 February 2020 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 27 October 2020 to © The Author(s) 2020 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0.