page_title: Surveillance issues in smart cities
section_title: Predictive policing
hierarchical_section_title: Surveillance issues in smart cities / Law enforcement / Policing Techniques / Predictive policing
caption_attribution_description: English: New York City Police officers being debriefed by their lieutenant (in the white shirt) in Times Square, May 29, 2010. This photo was created by Luigi Novi. It is not in the public domain, and use of this file outside of the licensing terms is a copyright violation. If you would like to use this image outside of the Wikimedia projects, you may do so, only if I am properly credited, either by linking the photograph to this page, or with an easily visible credit placed near the photo in each instance in which it is used. Please credit authorship as follows: &#160;© Luigi Novi / Wikimedia Commons. Please maintain the original file name in all uses. You can see a gallery of some of my other photos here. If you have any questions, you can contact me by sending me an email or leaving a note at the bottom of my talk page.
context_page_description: Smart cities seek to implement information and communication technologies in order to improve the efficiency and sustainability of urban spaces while reducing costs and resource consumption. In the context of surveillance, smart cities monitor citizens through strategically placed sensors around the urban landscape, which collect data regarding many different factors of urban living. From these sensors, data is transmitted, aggregated, and analysed by governments and other local authorities in order to extrapolate information about the challenges the city faces in sectors such as crime prevention, traffic management, energy use and waste reduction. This serves to facilitate better urban planning and allows governments to tailor their services to the local population.
  Such technology has been implemented in a number of cities, including Santa Cruz, Detroit, Barcelona, Amsterdam and Stockholm. Smart city technology has developed practical applications in improving effective law enforcement, the optimization of transportation services, and the improvement of essential infrastructure systems, including providing local government services through e-Governance platforms.
context_section_description: Predictive techniques in policing are not new, as search warrants are a pre-existing example of authorities acting on the basis of suspicion and prediction in contemporary communities In the context of smart cities, predictive policing is the use of data analytics to determine potential locations of future crime. This data collection often occurs through smartphones carried around by urban populations. Through location-based services in smartphones, the movements of individuals can be tracked and scrutinized by authorities. This has the potential to be particularly effective in crowd control. By comparing the different velocities of individual smartphone users within a certain location, it is possible for law enforcement authorities to ascertain crowd density. This allows for targeted crowd management and prediction of dangers related to excessive crowding. Police are thus able to take appropriate action (such as information broadcasting) in order to reduce the threat of injuries from incidents (such as crowd stampedes), as well as crowd related crime (such as thefts) from occurring.
  This type of policing also allows for law enforcement agencies to ‘predict’ where, when or by whom a crime may occur in the future and respond accordingly. Big data analytical tools are used to identify patterns in crime, allowing authorities to map high risk areas, times and days for certain types of crime. Through such software, police are also able to create profiles of potential criminals and associated behaviours. Developments in technology within smart cities allow for the scope of predictions to be increased as well as the types of responses available to law enforcement bodies.
  Experiments conducted in response to a ‘predictive policing algorithm’ based on crime data in Santa Cruz, California, enabled police officers to identify the most likely time and place within a certain locality for a particular crime to be committed. This allowed for targeted patrols to be made with a 4 percent decline in burglaries and 13 additional arrests being recorded within the first 6 months. These figures are preliminary however, and do not account for unreported crime or crime that was prevented through increased police presence.
  Although it is possible to envisage such law enforcement intervention as becoming the norm where smart city surveillance technologies have been adopted and implemented, predictive policing has raised a number of legal and non-legal controversies. Firstly, the level of criminal activity in a particular area sufficient to warrant extra patrols is unclear when predicting the commission of offences. The point at which the probability of crime becomes statistically significant is one which legal scholars and courts alike have had trouble defining. Within this framework, there is a degree of arbitrariness upon which the weight of the predictive data analysis must be considered, as high crime areas can only be defined with reference to “low levels of crime”.
  Further, in the United States, searches and arrests must be made on grounds of reasonable suspicion under the Fourth Amendment. This means that officers must be able to “point to specific and articulable facts” that “warrant the intrusion”, or make a predictive judgment that the person is in possession of an item that related to the commission of an offence. Similar protections, though not constitutionally based, exist in Australia as well as the UK. The latter was confirmed as binding by the European Court of Human Rights on a number of European nations, which includes civil law states. The ability to formulate such “reasonable suspicion” on the basis of big data algorithms is controversial, with some critics arguing that in the absence of active police corroboration of predictive forecasts, there are insufficient grounds to warrant an arrest. Further, the general nature of predictive forecasts is arguably incompatible with the acceptable standards outlined by the United States Supreme Court with respect to specific individuals. Patterns of crime generated through data analytics
caption: A data-driven stop and frisk program in New York was found to constitute racial profiling.
exif: {"Image Make": "CASIO COMPUTER CO.,LTD", "Image Model": "EX-Z750", "Image Orientation": "Horizontal (normal)", "Image XResolution": "72", "Image YResolution": "72", "Image ResolutionUnit": "Pixels/Inch", "Image Software": "1.02", "Image DateTime": "2010:05:29 16:42:27", "Image YCbCrPositioning": "Centered", "Image ExifOffset": "282", "Image PrintIM": "[80, 114, 105, 110, 116, 73, 77, 0, 48, 51, 48, 48, 0, 0, 0, 5, 0, 1, 0, 22, 0, 22, 0, 2, 1, 0, 0, 0, 1, 0, 5, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 16, 129, 0, 0, 0]", "Thumbnail Compression": "JPEG (old-style)", "Thumbnail XResolution": "72", "Thumbnail YResolution": "72", "Thumbnail ResolutionUnit": "Pixels/Inch", "Thumbnail JPEGInterchangeFormat": "33726", "Thumbnail JPEGInterchangeFormatLength": "8630", "EXIF ExposureTime": "1/25", "EXIF FNumber": "14/5", "EXIF ExposureProgram": "Program Normal", "EXIF ExifVersion": "0221", "EXIF DateTimeOriginal": "2010:05:29 16:42:27", "EXIF DateTimeDigitized": "2010:05:29 16:42:27", "EXIF ComponentsConfiguration": "YCbCr", "EXIF CompressedBitsPerPixel": "112/25", "EXIF ExposureBiasValue": "0", "EXIF MaxApertureValue": "3", "EXIF MeteringMode": "Pattern", "EXIF LightSource": "Unknown", "EXIF Flash": "Flash did not fire, compulsory flash mode", "EXIF FocalLength": "79/10", "EXIF FlashPixVersion": "0100", "EXIF ColorSpace": "sRGB", "EXIF ExifImageWidth": "1600", "EXIF ExifImageLength": "1200", "Interoperability InteroperabilityIndex": "R98", "Interoperability InteroperabilityVersion": "[48, 49, 48, 48]", "EXIF InteroperabilityOffset": "33602", "EXIF FileSource": "Digital Camera", "EXIF CustomRendered": "Normal", "EXIF ExposureMode": "Auto Exposure", "EXIF WhiteBalance": "Auto", "EXIF DigitalZoomRatio": "0/0", "EXIF FocalLengthIn35mmFilm": "38", "EXIF SceneCaptureType": "Standard", "EXIF GainControl": "Low gain up", "EXIF Contrast": "Soft", "EXIF Saturation": "Hard", "EXIF Sharpness": "Normal"}