Vincenzo Alaimo explores how open data can be used as a tool to promote smart city development. Read his article “Open Data Smart City” in this month’s issue of Municipal World! https://issuu.com/municipalworld/docs/mwdigestmar2017
- Salary will be pretty standard for Queen’s University postdocs, so it won’t be enormous! However you will get the opportunity to work with the Surveillance Studies Centre at Queen’s, and be funded to conduct intensive, directed research and produce publishable work solely and jointly with me, attend international conferences, and more.
- I am looking for someone who will spend large portions of their time in New York working on that case-study (one of three cities being studied by the project). You should ideally therefore already have both Canadian and US citizenship or necessary work or residency permits for the country in which they do not have citizenship.
- There’s really little time for training or induction involved and you will need to be fully prepared to hit the ground running, i.e. you will already have conducted significant doctoral or post-doctoral work on Smart Cities AND/OR The Internet of Things AND/OR contemporary urban surveillance & security.
- You need to be a social scientist comfortable with interviewing and observational / ethnographic work AND understand the technologies involved in Smart Cities enough to conduct interviews with developers, policy-makers and users in government, police and so on.
In light of a recent statement made by James Clapper, the US Director of National Intelligence, it has been made apparent that United States government and intelligence agencies maintain a stranglehold on surveillant practices, despite use of encryption and recent backlash by citizens regarding government privacy infringements. As reported by the Guardian, Clapper confirmed that Internet of Things devices allow for surveillant practices by intelligence agencies.
“In the future, intelligence services might use the [internet of things] for identification, surveillance, monitoring, location tracking, and targeting for recruitment, or to gain access to networks or user credentials,” explains James Clapper at his annual “assessment of threats” against the US (Guardian 2016).
This includes law enforcement agencies which have increasingly requested court orders which request companies to provide data collected from citizens. As noted by Trevor Timm, companies such as Fitbit and Dropcam, have been requested or have provided user data to legal authorities (Guardian 2016). What should be noted, is the type of information gathered by IoT devices, which fuels privacy concerns. Billions of IoT devices currently exist, “each of which are designed to harvest, store, and communicate a wealth of data” (Maras 2015:102). This data provides real-time information regarding a user’s “health and finances, locations, contacts, habits, behaviours, and activities” (Maras 2015:102) essentially mapping “patterns of life” (Amoore 2013:109). Very simply, the collected information by Internet of Things devices is, in effect, big data. Although never mentioned in any related literature as such, upon analyzing the content that the Internet of Things collects and comparing them to contemporary definitions of big data, distinct similarities can be drawn since data that is collected, “is more information than any individual human or group of humans can comprehend” (Andrejevic 2014:1675). Not to mention, several of these devices are vulnerable to hacking and other exploits.
As a result, a landscape has been created in which private information is constantly collected, stored, analyzed and monitored, as well as shared with a variety of other IoT devices, users and third parties (Maras 2015:102). Yet, users are potentially left without a full understanding of the implications of such a massive gathering of data. This excessive amount of data collection has raised several questions and concerns regarding privacy and surveillance. Users are not made aware of who benefits from this data, to whom this data is collected by, who it may be given to, when this data is collected and the potential outcomes of data collection. More than this, user profiling and targeting as well as social sorting are amongst other negative consequences of mass data gathering.
Although James Clapper’s statement is not a surprising revelation within the academic community, its importance lies in increasing public awareness regarding surveillant practices used by government and surveillance agencies. It brings to light how big data and IoT devices, which may provide many practical benefits, may, in some circumstances, be used to monitor citizens, and ultimately infringe on their privacy.
This results in a paradox where privacy and the Internet of Things cannot completely coexist (Wiseman 2013:8). There is a trade-off. In exchange for better, more feasible and more reliable services, a user must relinquish certain details about themselves. But, it must be reiterated that privacy is sacrificed in exchange for the tangible benefits offered by IoT devices. With this, a double edged sword is presented. With each incremental piece of information provided to Internet of Things devices and services, the better these services become. Yet, through this constant dissemination of private information, the more privacy is lost. Wiseman expresses how a technology which may not have initially been intended to pervade user privacy, may easily be reconfigured to ‘creep’ its users. “The purpose of the IoT to realize a smooth functioning information society may (also) turn into the perfect tool to realize a surveillance society” (Wiseman 2013:2). With such a vast amount of information, it is easy to understand how seemingly useful technology may actually be used as instruments for surveillance (Wiseman 2013:9).
IoT devices are starting to gain popularity as they begin to penetrate households, cities and various other aspects of day to day life. Thus, this public announcement by Clapper serves to inform citizens of the potential nefarious traits embodied in convenient gadgets.
Amoore, Louise 2013 ‘Security and the claim to privacy’ International Political Sociology8(2) 108-112
Andrejevic, Mark 2014. Big data, big questions “The Big Data Divide”. International Journal of Communication, 8(0):1673-1689
Maras 2015. “Internet of Things: Security and Privacy Implications” International Data Privacy Law 5(2):99-104
Timm, Trevor. 2016. “The government just admitted it will use smart home devices for spying” The Guardian Retrieved February 24, 2016 (http://www.theguardian.com/commentisfree/2016/feb/09/internet-of-things-smart-devices-spying-surveillance-us-government)
Wiseman, T.H.A. 2013. “Purpose and function creep by design: Transforming the face of surveillance through the Internet of Things”, European Journal of Law and Technology 4(2)
Deputy Chief Peter Sloly believes the Toronto Police Service could reduce it’s force by ‘several hundred’ officers if it leverages technologies associated with ‘Big Data’ (CBC 2016). Sloly claims big changes are needed to restore trust in policing he feels is at a low point not just in Toronto, but also, across North America (Powell 2016). Investigations into the killing of Laquann McDonald by a Chicago police officer and Sammy Yatim by a Toronto police officer have damaged public perception and generated traction for calls for reform. In both cases, human resources management and new information communication technologies have been presented as solutions to the challenges of contemporary policing.
Many technology firms are making claims that advancements in data analytics can shift police forces from a reactive model to a predictive one. Through Big Data, the City of Chicago has produced a list of individuals that algorithms have categorized as high risk for committing serious crime. The Chicago Police Department (CPD) then contacted the individuals with information about the consequences of the criminal acts they were deemed likely to commit in an effort to change their ‘future’. At the IBM Smarter Cities conference in Las Vegas last year it was announced that Watson analytics would mine data provided by Twitter in an attempt to predict crime hot spots.
Chicago, like Toronto, has a body worn camera pilot underway. Manuel launched the body worn camera pilot not long after the death of Laquan McDonald, claiming that the new technology would help restore faith and trust in the police force. Interestingly, the ‘in car camera’, an earlier form of mobile surveillance, was introduced twenty years prior in the State of Illinois with the same objective: restore the loss of faith and trust in local law enforcement. In some ways, these mobile cctv solutions are closer to the reactive policing model cited by Sloly. Although footage of interactions between officers and citizens has proven useful in both the McDonald and Yatim cases, the video is a record of reactive policing in action.
Days after video of a Chicago Police Officer shooting Laquan McDonald sixteen times was released, Chicago Mayor Rahm Emanuel fired superintendent Garry F. McCarthy (Davey 2015). The footage was suppressed for a year after McDonald’s death, which, in conjunction with reports of officers deleting private CCTV footage at a nearby Burger King and threatening witnesses to make them file false accounts, fuelled outrage and protests in the city. In Toronto, just days after a verdict in the Sammy Yatim case was released, Mayor John Tory made a public appearance at the Toronto Police College. Tory observed training that focuses on dealing with people in crisis situations similar to Yatim’s case. Although the mayor was impressed he stated that more needed to be done to improve policing.
Like Tory, Sloly believes there is work to be done in the Toronto Police Force to foster new cultural norms. Sloly claims the practice of carding is reflective of a global crisis in policing. Research has repeatedly shown carding perpetuates systemic racial bias and is a result of inadequate training and supervision (Floyd v. State of New York; R. v. Fountain; Ontario Human Rights Commission 2003; Wortley and Owusu-Bempah 2011). In other words, officers were able to systematically target individuals for criminal investigation based on skin colour.
According to many creators of smart technologies, algorithms are not susceptible to bias (Kitchin 2014). Following this logic, a Big Data approach to policing could offer much to Toronto Police Services. However, scholars have contested the claims that algorithms ‘tell it like it is’ and encourage researchers to challenge claims of objectivity (van Dijck 2014). Transparency in the collection, sharing and analysis of data is an important safeguard against the potential failures of Big Data (Couldry and Powell 2014). These failures are already apparent in smart policing projects in the United States (Ferguson 2015). Thus, inadequate training and supervision of Big Data policing could reproduce the issues that have persisted with carding historically. Unfortunately, discussions about the potential for Big Data to erode democratic freedoms through the intensification of surveillance remain marginalized (Lyon 2014). Deputy Sloly, and those in the Toronto Police Service that favour his position, would do well to encourage researchers to join the table as the seemingly inevitable move to Big Data and smart policing occurs.
CBC. (27012016). Police “trying to dissolve the uniform,” Tory says of crisis training. Retrieved February 3, 2016, from http://www.cbc.ca/news/canada/toronto/john-tory-police-college-1.3422033
Couldry, N., & Powell, A. (2014). Big Data from the bottom up. Big Data & Society, 1(2), 2053951714539277. http://doi.org/10.1177/2053951714539277
Davey, M. (2015, December 1). Chicago police superintendent fired in response to shocking video of black teen being shot 16 times. Retrieved from http://news.nationalpost.com/news/chicago-police-superintendent-fired-in-response-to-shocking-video-of-black-teen-being-shot-16-times
Ferguson, A. G. (2015). Big Data and Predictive Reasonable Suspicion. University of Pennsylvania Law Review, 163(2), 327–410.
Floyd v. State of New York. 82 Fed. R. Serv. 3d (West) 833 (S.D.N.Y. 2012).
Lyon, D. (2014). Surveillance, Snowden, and Big Data: Capacities, consequences, critique. Big Data & Society, 1(2). http://doi.org/10.1177/2053951714541861
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481. http://doi.org/10.1177/2053951714528481
Ontario Human Rights Commission. 2003. “Paying the Price: The Human Cost of Racial
Powell, B. (2016, January 18). Passed over for the top job with Toronto police, Sloly says harnessing technology could allow the service to drop “several hundred police officers.” The Toronto Star. Retrieved from http://www.thestar.com/news/gta/2016/01/18/deputy-chief-peter-sloly-pushes-for-change-amid-low-point-and-looming-crisis.html
R. v. Fountain, 2013 ONCJ 434
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Wortley, S. and Owusu-Bempah, A. 2011. “The usual suspects: police stop and search practices in
Canada.” Policing and Society 21(4):395-407.
The idea for my first Ubicity blog post was initially inspired by a piece I stumbled upon written by Brian Jackson, doctor titled ‘What does a Liberal government mean for Canada’s Technology Policy?’ Given Canada’s new leadership under Liberal party leader, Justin Trudeau, along with my current research interests, broadly being open data and open data initiatives, the timing was no more fitting for me to explore the Liberal party’s plan for openness, open data and government transparency. My curiosity was piqued to examine the Liberal party’s action plan for a ‘fair and open government’, along with its aims and ramifications for openness and transparency.
My investigation of the Liberal party platform began with a trip to the Liberal party of Canada’s website. Neatly laid out and easily accessible were three main categories which explain the party’s aims in different aspects of citizen life and political stances. Among these was the category title, ‘Open, Honest Government’. This section provides a brief summary of what openness and transparency entails according to the Liberal party. Naturally, criticism of former Prime Minister, Stephen Harper’s approach to openness populates this section, but for good reason. As witnessed over the past decade, expectations of Canadian citizens went unattended under Conservative leadership, and is an obvious place for adjustment under new leadership.
The Liberal party seems to place transparency at the forefront of their action plan. They seemingly offer mutual trust and cooperation between government and citizen, and use inviting and inclusive words such as ‘us’ and ‘together’ in order to demonstrate the communal progress that will be made between both. They their action plan to be, “…a sweeping agenda for change”, and provide an in depth outline of a diversity of adjustments to be made under new leadership. These changes touch on many ways the Liberals intend on implementing “a fair and open government”. Specific to my research interests were the sections discussing the Liberal government’s encouragement of government openness and transparency. This agenda asserts that transparency will stand as a fundamental principle for the Liberal Party platform.
Policies regarding open data seem promising. For instance, Access to Information will see rejuvenation, as data will be made available to Canadians by default. Even more, this agenda seeks to “accelerate and expand open data initiatives”. This is certainly exciting since the value of open data initiatives will possibly see its full potential. Open data holds the potential to integrate citizen and government together in order to assist in the shaping and constructing of a smarter city, converting previously unconnected data into actionable information for usage in the urban landscape (Dodgson and Gann 20117:4), and enhancing city services including electrical and water consumption, waste management, or public transportation (Santoso and Kuehn 2013:2). Within smart city promotional discourse, the city is seen as a system of systems which could benefit from extensive information management tools (ibid: 5), the promotion of innovation in the planning and management of cities (Naphade et alt 2011:1), encouragement of environmental stability (Harter 2010), as well as convivial living conditions. With these accelerated initiatives, these potentialities may actually come to fruition.
In addition to these objectives, the Liberals plan for a revamping of Canada’s Access to Information Act and increased transparency of parliamentary plans and expenses. Not only does this action plan stipulate that government data will be available to all Canadians, but it will also make also readily available citizen’s own personal information. Upon reading these reforms, it becomes clear that this political party seeks to engage with Canadian citizens and ensure that government data and services will become more accessible. It is a step in the right direction for Canadian governance as the Liberals seem to have interest in increasing openness and transparency in an effort to improve trust and accountability of government.
Note my wariness of the current government’s promises. Although promises for openness and transparency have been made, one must keep in mind that this government is newly elected. This is to say, it is too early to understand how effective the current government is at following its platform, tending to citizen’s concerns and whether or not the current government truly takes matters regarding openness, open data and transparency seriously. On paper, the Liberal party appears to be optimistic about the possibilities of openness and transparency, and I too share the same sentiment, as the possibilities to be had with open data currently have yet to see their full potential. With time it will be witnessed if and how the current government will instill this action plan. In all, the liberal policy for a fair and open government seems quite promising, if all goes as stated. With promises of encouraged collaboration between citizens and government, an acceleration of open data initiatives and a focus on openness and transparency, this platform is surely a refreshing breath for Canadians.
Dodgson, M. and Gann, D. 2011 “Technological Innovation and Complex Systems in Cities,” Journal of Urban Technology, 18(3): 101-113.
Harter, G. Sinha, J., Sharma A. and Dave, S. 2010. Sustainable Urbanization: The Role of ICT in City Development. New York: Booz & Co.
Jackson, Brian. 2015. “What does a Liberal government mean for Canada’s technology policy?” IT World Canada Retrieved October 22, 2015 (http://www.itworldcanada.com/article/what-does-a-liberal-government-mean-for-canadas-technology-policy/377808)
Liberal Party of Canada. 2015. “Openness and Transparency” Retrieved October 22, 2015 (http://www.liberal.ca/openness-and-transparency/)
Naphade, M., Banavar, G. Harrison, C. Paraszczak, J. and Morris, R. 2011. “Smarter Cities and Their Innovation Challenges,” IEEE Computer, June 2011: 32-39.
Santoso, S. and Kuehn, A. 2013. “Intelligent urbanism: Convivial living in smart cities.” iConference 2013 Proceedings: 566-570.
The winners of the 2015-16 IBM Smarter Cities Challenge were announced by Jen Crozier at the IBM Edge event in Las Vegas, Nevada last month. The Smarter Cities Challenge is the largest philanthropic program IBM currently runs, through which the firm claims to have donated services worth US$50 million to over 100 cities in the past five years. In addition to announcing the winners, Crozier also announced that each of the sixteen municipalities will receive expert advice on using Watson analytics as part of the prize package. A special bonus offering for three of the winners was also announced: Detroit, Memphis and Melbourne will be gifted with current and historical data sets from Twitter. These data sets will be analyzed with the help of Watson analytics and a Twitter analyst that will be on loan to one of the cities for three weeks. The ‘landmark partnership’ between IBM and Twitter was announced at the end of 2014 aims to ‘transform how businesses and institutions understand their customers, markets and trends’.
According to IBM marketing materials, Watson offers predictive analytics for a range of contexts including helping help retailers predict the behaviour of customers; predicting insurance claims in relation to sever weather; and predicting maintenance requirements for urban infrastructure. The spatiotemporal metadata associated with tweets has shown predictive value for a range of situations including elections, disease outbreaks, political revolutions and urban security threats (Gerber 2014).
Twitter data was famously used for urban security purposes during the G20 protests in Toronto in 2010, which involved surveillance of users and their social networks (Bennett, Haggerty, Lyon and Steeves 2014). Data posted on Twitter was used by security forces to prevent select protestors from even approaching security zones and used as evidence in the prosecution of suspicious and risky individuals (Werbin 2011). Twitter data was front and centre of the high profile case of Byron Sonne, who claimed to be ‘testing’ the security apparatus with his online Twitter activity; it took two years for him to be cleared of criminal charges linked to the incident (CBC 2012).
Beyond national security threats and mega event attacks, researchers are demonstrating the potential for Twitter data to predict criminal activity on a much smaller scale. Traditionally, predicting urban crime levels has involved analyzing historical crime data for a particular geography in combination with demographic information for that geography and extrapolating the results into the future. The Kernel Density Estimation model is the preferred approach for many researchers constructing crime hot spots (Andresen 2015). According to Hart and Zandbergen (2014) this may be attributed to the availability of this function in GIS applications, perceptions of accuracy and ease of use. Kernel Density Estimation (KDE) uses the historic crime data tied to a particular geography to predict the future probability of criminal activity in that space. Researchers have problematized this approach by arguing hot spots do not accurately portray the geography of criminality, which can be improved by using spatiotemporal data (Wang, Gerber and Brown 2012; Bogomolov et al. 2014; Malleson and Andresen 2015).
With the adoption of smart phones and social media applications, oceans of spatiotemporal data have become available to researchers. One of the earlier and more often cited articles on using data from Twitter for the purpose of predicting crime is the work of Wang, Gerber and Brown (2012). Their experiment involved semantic analysis of tweets collected from news agencies in combination with data from the local law enforcement agency in Charlottesville, Virginia. Although the researchers were only able to demonstrate predictive values for a limited range of crimes (‘hit and runs’ and ‘break and enters’), the research was promising (Wang, Gerber and Brown 2012). Since the tweets they used were posted by journalists, the analysis did not benefit from spatiotemporal meta data.
However, two years later, Matthew Gerber (2014) published his own research (funded by the United States Army) that combined historical crime data in the City of Chicago with semantic analysis of tweets and their spatiotemporal meta data. He was able to improve predictions for 19 out of 25 categories of crime (Gerber 2014). In the same year a team of researchers demonstrated mobile data could be used for crime prediction and could increase the granularity of hot spots using London as a case study (Bogomolov et al. 2014). They produced hot spot maps that looked less like the smooth contours of a weather forecast and more like a Rubik’s cube. According to Malleson and Andresen (2015), using spatiotemporal data leverages the characteristics of ambient populations to improve the predictive values for mobile crimes. They claim that by combining historical crime data with spatiotemporal data from Twitter predictions of criminal activity in the City of Leeds were improved (Malleson and Andresen 2015).
The use of data from social networking sites like Twitter to predict criminal hot spots is clearly not a magic bullet for local law enforcement agencies. As Malleson and Andresen (2015) note this data is not reflective of the entire ambient population in a given spatiotemporal cluster. However, traditional hot spot analysis relies on historical data that is not reflective of an actual spatiotemporal cluster either. There are other limitations, which include the challenge of decoding the meaning of each tweet (Gerber 2014). Despite these limitations, as local law enforcement agencies struggle to allocate limited resources the potential for creating more precise hot spots through data analytics will likely be of interest. The city chosen for the bonus consultation from Twitter may have the opportunity to explore the possibilities that the research examined above points to.
Andresen, Martin A. 2015. “Identifying Changes in Spatial Patterns from Police Interventions: The Importance of Multiple Methods of Analysis.” 16(2):148–60.
Bennett, Colin J., Kevin Haggerty, David Lyon, and Valerie Steeves. 2014. Transparent Lives: Surveillance in Canada. Edmonton, Canada: Athabasca University.
Bogomolov, Andrey et al. 2014. “Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data.” arXiv:1409.2983 [physics]. Retrieved June 17, 2015 (http://arxiv.org/abs/1409.2983).
CBC. 2012. “G20 Protestor Byron Sonne Cleared of All Charges.” CBC News. Retrieved June 18, 2015 (http://live.cbc.ca/Event/G20_hearing).
Gerber, Matthew S. 2014. “Predicting Crime Using Twitter and Kernel Density Estimation.” Decision Support Systems 61:115–25.
Hart, Timothy and Paul Zandbergen. 2014. “Kernel Density Estimation and Hotspot Mapping.” Policing: An International Journal of Police Strategies & Management 37(2):305–23.
Malleson, Nick and Martin A. Andresen. 2015. “Spatio-Temporal Crime Hotspots and the Ambient Population.” Crime Science 4(10):1–8.
Wang, Xiaofeng, Matthew S. Gerber, and Donald E. Brown. 2012. “Automatic Crime Prediction Using Events Extracted from Twitter Posts.” Pp. 231–38 in Social Computing, Behavioral – Cultural Modeling and Prediction, Lecture Notes in Computer Science, edited by S. J. Yang, A. M. Greenberg, and M. Endsley. Springer Berlin Heidelberg. Retrieved June 17, 2015 (http://link.springer.com.proxy.queensu.ca/chapter/10.1007/978-3-642-29047-3_28).
Werbin, Kenneth C. 2011. “Spookipedia: Intelligence, Social Media and Biopolitics.” Media, Culture & Society 33(8):1254–65.
The Toronto Police Service has launched a yearlong pilot project to evaluate the utility of body-worn cameras at a cost of CAN$500,000. The pilot involves 100 police officers testing three varieties of body-worn cameras in their daily work. Technology firms Panasonic, Mediasolv and Integrys manufacture the body-worn cameras being trialed. The project was developed in response to recommendations made in a variety of reports, including one submitted last summer by Justice Iacobucci who suggested the technology could enhance the transparency of policing for officers and citizens alike. The Toronto Police Service claims, “Body-worn cameras are unbiased, reliable eyewitnesses to community interactions with the police. They will provide reassurance to community members and police officers”. Area Field Superintendent Tom Russell sees a range of opportunities for using the body-worn camera including apprehension under the Mental Health Act, arrests, engaging with persons in crisis, crimes in progress, and public disorder. The police service pushed out details relating to how the officers are being trained to use the new technology, and how data the devices collect will be handled, through press releases, blog posts, online videos and an FAQ sheet.
A photo of the devices being tested during the Toronto Police Service body-worn camera pilot project.
According to a video published by the service, officers received 32 hours of training to prepare for deployment of the body-worn camera technology, which included theoretical and technical instruction. The service states it consulted the Privacy Commissioner of Ontario and the Ontario Human Rights Commission during the design stage of the pilot. In February the Office of the Privacy Commissioner of Canada released a report on body worn camera use in policing, which states there are ‘serious implications for individuals’ right to privacy’. Staff Sergeant Michael Barsky, who is the project lead, said, “It is very important we protect the privacy interests of the citizens of Toronto as well as consider the human rights issues that may arise”. A review of the FAQ document published by the Toronto Police Service suggests some consideration has been given to issues of privacy in general, and to the capture, storage and review of body-camera data in particular.
Officers have been instructed to start recording when they begin an investigation and stop recording when the investigation completes or ceases to produce relevant information. When entering a private home, officers are required to stop recording if asked to do so by the occupant. While recording, data collected cannot be ‘accessed, reviewed, edited or deleted’. All data is downloaded from the device at the end of the shift, encrypted and stored on a server owned and operated by the Toronto Police Service for a period of one year unless it is required for an investigation. After that point, only the officer who collected the data and their supervisor will have access to the recording, and only select members of a technical team will be able to edit it. Citing R v Stinchcombe, the FAQ says all data will be included in disclosure for court proceedings.
The Toronto Police Service is not an early adopted of body worn video (BWV) in Canada. In 2011, the Edmonton Police started the first federally funded BWV project in Canada, which ran for four years. Dr. Emily Stratton, co-ordinator of the BWV project for Edmonton Police Service, claims that when individuals are under the influence of drugs or medication they are either indifferent to, or excited by, the use of BWV. However, it is not only the public that BWV use is intended to modify. Barack Obama recently committed US$250 million to supply the nation with 50,000 body-worn cameras, an initiative sparked by a wave of citizen deaths involving police officers, most notably Freddie Gray of Ferguson. Obama’s pledge came at the same time researchers at Cambridge University published their findings from a case study in Rialto, Calfornia, which showed a dramatic decrease in use of force by officers and a large reduction in grievances filed against officers.
The work of Ariel, Farrar and Sutherland in the Rialto case is promising. However, the mixed result of BWCs in different cases speaks directly to the inherently ambiguous nature of surveillance. There is danger in framing BWCs, as one of the Toronto Police Service project members has, as some kind of ‘truth tech’ that will provide ‘unbiased’ and ‘reliable’ testimony. It is also dangerous to suggest that BWCs can act as some kind of technological cloak that will protect innocent citizens from abuses of power exercised by rogue police officers. Law enforcement agencies must address the underlying issues that have brought policing to a moment where a technological silver bullet is in desperate need. Without a commitment to reforming human resource issues that propagate excessive use of force by police officers, BWCs will achieve little.
 Toronto Police Service, Body Worn Cameras: Frequently Asked Questions (Toronto: Toronto Police Service, May 2015), http://www.torontopolice.on.ca/media/text/20150421-body_worn_camera_faq.pdf.
 Toronto Police Service, Toronto Police Service Launches Year-Long Body-Worn Camera Pilot Project (Toronto: Toronto Police Service, May 15, 2015), http://torontopolice.on.ca/newsreleases/31840.
 Toronto Police Service, Body Worn Cameras: Frequently Asked Questions.
 Sara Faruqi, “Body Worn Cameras Start Rolling,” TPS News, May 15, 2015, http://tpsnews.ca/stories/2015/05/body-worn-cameras-start-rolling/.
 Toronto Police Service, “Body-Worn Cameras,” Toronto Police Service, accessed May 20, 2015, http://www.torontopolice.on.ca/bodyworncameras/.
 Faruqi, “Body Worn Cameras Start Rolling.”
 Toronto Police Service, “Body-Worn Cameras.”
 Body Worn Camera Pilot Project (Toronto, 2015), https://www.youtube.com/watch?v=UKY3scPIMd8&feature=youtube_gdata_player.
 Toronto Police Service, “Body-Worn Cameras.”
 Office of the Privacy Commissioner of Canada, “Guidance for the Use of Body-Worn Cameras by Law Enforcement Authorities – February 2015,” February 18, 2015, https://www.priv.gc.ca/information/pub/gd_bwc_201502_e.asp#ftn7.
 Faruqi, “Body Worn Cameras Start Rolling.”
 Toronto Police Service, Body Worn Cameras: Frequently Asked Questions.
 Royal Canadian Mounted Police, “Does Body Worn Video Help or Hinder de-Escalation?,” Royal Canadian Mounted Police Gazette, 2014.
 Office of the Press Secretary, “Strengthening Community Policing,” The White House, December 1, 2014, https://www.whitehouse.gov/embeds/footer.
 Barak Ariel, William A. Farrar, and Alex Sutherland, “The Effect of Police Body-Worn Cameras on Use of Force and Citizens’ Complaints Against the Police: A Randomized Controlled Trial,” Journal of Quantitative Criminology, November 19, 2014, 1–27, doi:10.1007/s10940-014-9236-3.
 Royal Canadian Mounted Police, “Does Body Worn Video Help or Hinder de-Escalation?”
 David Lyon, Surveillance Studies: An Overview (Cambridge: Polity Press, 2007).
Ariel, Barak, William A. Farrar, and Alex Sutherland. “The Effect of Police Body-Worn Cameras on Use of Force and Citizens’ Complaints Against the Police: A Randomized Controlled Trial.” Journal of Quantitative Criminology, November 19, 2014, 1–27. doi:10.1007/s10940-014-9236-3.
Body Worn Camera Pilot Project. Toronto, 2015. https://www.youtube.com/watch?v=UKY3scPIMd8&feature=youtube_gdata_player.
Faruqi, Sara. “Body Worn Cameras Start Rolling.” TPS News, May 15, 2015. http://tpsnews.ca/stories/2015/05/body-worn-cameras-start-rolling/.
Lyon, David. Surveillance Studies: An Overview. Cambridge: Polity Press, 2007.
Office of the Press Secretary. “Strengthening Community Policing.” The White House, December 1, 2014. https://www.whitehouse.gov/embeds/footer.
Office of the Privacy Commissioner of Canada. “Guidance for the Use of Body-Worn Cameras by Law Enforcement Authorities – February 2015,” February 18, 2015. https://www.priv.gc.ca/information/pub/gd_bwc_201502_e.asp#ftn7.
Royal Canadian Mounted Police. “Does Body Worn Video Help or Hinder de-Escalation?” Royal Canadian Mounted Police Gazette, 2014.
Toronto Police Service. “Body-Worn Cameras.” Toronto Police Service. Accessed May 20, 2015. http://www.torontopolice.on.ca/bodyworncameras/.
———. Body Worn Cameras: Frequently Asked Questions. Toronto: Toronto Police Service, May 2015. http://www.torontopolice.on.ca/media/text/20150421-body_worn_camera_faq.pdf.
———. Toronto Police Service Launches Year-Long Body-Worn Camera Pilot Project. Toronto: Toronto Police Service, May 15, 2015. http://torontopolice.on.ca/newsreleases/31840.
President Obama announced the Police Data Initiative today during a speech he delivered in Camden, New Jersey. Restructuring of human resource in the local police department has resulted in significant reductions of violent crime and the distribution of drugs. The President indicated that continued progress could be made through improvements to the force’s information technology management. He announced that the White House brought an ‘elite tech team’ that would work with the Camden police force to enhance their data management, including the integration of 41 separate systems it currently uses. President Obama claimed that data reform would ensure areas of the city that require additional law enforcement resources could be identified quicker and served better. Moreover, it was suggested that better data management will aid police forces in developing trust with local communities. Camden is one of twenty-one cities participating in the initiative:
According to the Office of Science and Technology Policy: “The lessons learned in Camden can help law enforcement around the country both by example and also directly since some of the development work can be shared through open source best practice.” The participating police forces will have two primary divisions of labour:
- “Using open data to increase transparency, build community trust, and support innovation
- “Better using technology, such as early warning systems, to identify problems, increase internal accountability, and decrease inappropriate use of force
Code for America is helping the police departments release 101 data sets that have not been accessible to the public until now. There are several open data practices included in the press release, including the creation of maps and hackathons. According to the release, Code for America is working with the International Association of Police Chiefs and the Police Foundation to “grow a community of practice for law enforcement agencies and technologists around open data and transparency in police community interactions.”
The initiative will also develop predictive analytics to identify ‘at risk’ officers in an effort to intervene before they break code of conduct. This is clearly a response to the recent media attention to excessive use of force, which President Obama gestured to in his mention of the situation in the City of Ferguson, among others. The University of Chicago is sending five data science scholars to several of the participating cities to enable them to develop analytics that will predict potentially problematic officers. The Department of Justice and other partners will apparently work with universities and other research firms to conduct research on body cameras and analytics of the video they will produce.
President Obama asserted that technology is only part of the solution in his address. He pointed to the need for society as a whole to address issues of race and racialization. President Obama also suggested the trend of increased sentencing for non-violent drug offence was usurping valuable financial resources that should be redirected from incarceration and invested in social programs. He also called attention to the social costs to communities that struggle as a result of broken homes. In his address the President was clearly acknowledging concerns about the use of force by local law enforcement agencies across the country. A point that is underscored by new policies designed to de-militarize local police forces.