Smart cities, smart policing?

I’m just back from lovely Barcelona where I was presenting a paper in a conference on smart cities organised by the UOC. It was a well organised event with a number of really interesting presentations. I learned indeed a lot. Daniel Quercia presented some truly amazing work on mapping familiarity of people with different parts of cities, perceptions of areas, and his work on smell maps (pointers here, here, here, and here). Maybe the ggplot2 graphics/maps had already biased me. Wim Vanobberghen talked about more bottom up approaches to smart cities and the notion of city labs. And Prof. Bert-Jaap Koops talked about privacy and data protection as a zombie and discussed a number of helpful avenues for re-thinking our ideas about these issues in today’s uber-connected society.

I presented a paper on smart cities and smart policing. You can find it here. The whole thesis is that for the most part policing has helped to shape and perpetuate our understanding of smart cities from a top down approach. This vision of smart cities emphasises a very technocratic vision “that is dictated by business potential, commercial logic and efficiency thinking” and it is “closely related to the technologically deterministic idea of a “control room” for the city”. In contrast more bottom up approaches emphasise the idea of the empowered individual.

The idea that integrated data sharing and analytical technology would help the police to do a better job is something anybody that has observed the transformation of police practice in the world of GIS can certainly testify to. GIS technology has certainly played a big role here in terms of how police communicates with the public (think the police.uk website), has managed performance (think CompStat and the target culture that dominated policing during the Labour years in the UK), and has informed analysis of and responses to crime problems (think hot spots policing). These applications, however, have not been in my humble opinion sufficiently critical or self-reflective.

I have several concerns in this regard. First, ok we know the data is problematic. Donald Campbell and Goodhart could have told you so. Yet we (even very smart people) tend to use it as if it is not. Recording has become so dodgy that UK Statistics Authority de-registered police data as national statistics and a parliamentary enquiry had to be launched. HMIC also looked into it (I can hear now my Spanish readers/colleagues weeping at the level of auditing and rigour that these enquiries and steps suggest). Critically, we have a limited understanding of the systematic part of bias on recording practices, which mean it is difficult to adjust for it in any model. To this we need to add the fact that we have a limited understanding of how spatio-temporal attributes affect reporting practices, certainly in the UK. The international literature suggest that there are neighbourhood factors that come into play here. So the noise is systematic. Therefore, we need to do better. We need more work on this, both in terms of understanding these factors, but critically in terms of thinking on how we can then use this knowledge to adjust our maps and local estimates. This has implications for communication to the public and crime analysis, but also for evaluation of strategies. We need to move away from hot spots evaluations that rely exclusively or primarily in just crime data.

Regarding communication specifically, I still think presenting crime maps to the public is a double edge sword. You could say this can be empowering and consistent with a more bottoms up approach to policing. And yes I’m all up for open data and transparency. Hell, I can be a pain about it. Ask Pepe Cid about my insistence to develop policies for SEIC requiring authors to deposit their data in open repositories. But we don’t live in a world of black and white. First, let’s remember the data are dodgy to start with and the general public is not being sufficiently informed in how this is the case when the data is presented to them. I also agree with Alex Singleton and Chris Brundson on the issue of spurious precision. At the very least, the maps should come with stronger health warnings NOT ON THE SMALL PRINT. Why do you have to click on “Use the Data” (you got to be a nerd to do that) and process … words of text before you are provided (a rather technical for the average Joe and Jane) of the process of locational anonyimisation? It is true I find the maps helpful for teaching purposes and to persuade students that Chris Grayling has no f*****g clue when he compares Moss Side with Baltimore in the 90s (i.e., a bad year in Moss Side is a good weekend in Baltimore and it is the bars in the city centre you need to avoid if you want to escape a bruising). Indeed, when I was buying a house, I did check the maps. I can be a bit hypocritical but not stupid. And yet, and yet… These maps will more or less correctly identify some areas as problematic. Do we really want to make that very clear to everybody with internet connection? I don’t know. There’s a literature on the impact of crime on house prices, insurance, and community reputations preventing investment and contributing to stigmatise their local residents. Ok, you don’t need the public crime maps for this. But they possibly don’t help either. Maruna talk about how in the process of desistance offenders engage in redemption scripts that in some ways recount their personal history. Would this be possible if you tattoo in their forehead what they have done in the past? I think that at the very least we need to be asking these questions rather than assume that just because there may be some benefits to publicise crime maps we should go down that route. When we were doing our ethnographic work on gangs, many of the regeneration agencies we encounter were adamant that in their areas they didn’t have gangs (they did). But there’s a reason why they put forward that vision. Their job is to regenerate areas and this kind of characterisations are not terribly helpful in the process. Let’s also not forget these maps help to reinforce a particularly narrow and skewed vision of what crime is all about (you won’t see in police uk a map of fraud or insider trading) and what the police mission is all about (chasing crime: contrast this with Jerry’s presentation, start around minute 12). Although, of course, we cannot map what matters, if we don’t really measure what matters.

Critically, I’m not clear how these models and uses can be made compatible with a more bottom-up, participatory and democratic understanding of smart cities (and by that extension of policing). I think the future is concerning. The increasing availability of new surveillance “sensors” raises important questions about privacy. The shift from trying to detect hotspots to predicting individual offending (particularly within a policing context, in corrections and probation this is far from new) raises equally concerning questions. And this is not just a US thing. The MET, at the very least, explored similar technologies (check the presentation from Muz) in the context of gang crime with the help of Accenture. It is not only civil libertarians that are concerned with these applications of data mining techniques. Machine learning and data scientists are also increasingly aware of the implications of their models and have initiated an ongoing debate about fairness and transparency of algorithms used for decision making on areas that matter to citizens. Initiatives such as data justice are worth keeping an eye on. There is money to be made by selling predictive modelling to the police. And some are taking an unashamedly aggressive and partly misleading approach to the branding and marketing of their products. There is indeed potential to be explored. But is essential we take a more critical and reflective approach and not be seduced by the sirens of technological progress. The history of policing is full of errors that resulted in skewed priorities associated with the adoption of new technologies. Let’s learnt from the past. Let’s experiment. But let’s not rush to put “solutions” into production and let’s think about not only whether these solutions “work” but also whether they are fair and just.

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