Time to Stand out from the Crowd. The Fight for Privacy & Autonomy in the 21st Century.
How Smart Home Cameras Produce a Consumer Policing Model.
Time to Stand out from the Crowd. The Fight for Privacy & Autonomy in the 21st Century.
Time to Stand out from the Crowd. The Fight for Privacy & Autonomy in the 21st Century.
Time to Stand out from the Crowd. The Fight for Privacy & Autonomy in the 21st Century.
The Following is an outline to the graphs and maps depicted on this site:
A. Home Surveillance Graphs
B. Interactive Ring Map- Including:
C. Dallas and U.S property Crime Graphs
D. DFW Crime Heat Maps - Dangerous vs. Good Areas
E. Dallas Districts Crime Charts via DPD Open Data Portal
F. Dallas Districts Data and Demographics
G. Dallas Districts Crime Vs. Ring Density Maps
H. Dallas Safest Vs Most Dangerous Neighborhoods based Upon Ring Data
I. Dallas District Maps and Re-Disticting Maps
J. DFW Police and Ring Partnerships
K. 2022 Current Surveillance Graphs
The various graphs, maps and charts on this site push the viewer to see the data collected in a visualized manner. In doing so, the observer can interpret and visualize the data in a way that collectively can be compared to one another. Consequently, the visual representations permit visitors to better grasp the true meaning and the importance of the data collected. In particular, the ways in which I have analyzed the data into clearer representations/comparisons within separate, but distinct micro categories provides new ways to interpret the data. In turn, this unwavering approach to view data in a micro level help to recognize the filler categories that misleads users when viewing data as a whole. The most important issues that I want to raise with the graphs is that Amazon Ring cameras do not deter crime. It is crucial to note that other areas of Dallas have higher crime rates while also possessing higher Ring density rates and/or areas of low property crime have low Ring density rates. This is essential when it comes to viewing the DPD data to determine 'dangerous' vs. 'safe' neighborhoods. Partnerships with Amazon and police push the narrative around frightened and prejudiced language to normalize surveillance as a means to deter crime in selective communities. The alarming language around 'safe' versus 'dangerous' directly correlates between minority and lower income demographic areas in comparison to white and higher income districts. Again, there is no direct correlation that Ring prevents crime in safe or dangerous communities. My aforementioned charts within the DFW districts that incorporate various household incomes and demographic sets does not prove Ring and police partnerships avert crime.
On the contrary, Police and Ring partnerships demonstrate that the focus of crime prevention is a guise to normalize surveillance by pushing fear within communities by creating broken window policing. As community members become more afraid, police and Ring continue to create digital dossiers, collect data, and issue/advocate surveillance in areas that police need more oversight. What the graphs show, is that police and Amazon both hide behind the data that is collected and how it is used against residents or users. Secondly, the graphs also make aware how DPD uses data as they see fit when it comes to property crime as well as when it comes to Ring footage that is viewed by police which can be hacked. The safety of Residents and Ring users are not a priority. The priority is the continuation and normalization of surveillance coupled with a heightened fear of crime. After observing the data collected from the Dallas Police Department and the open data portal, it is obvious that the intensive language around crime is used to the advantage of DPD. DPD uses the data as they see fit to focus on minority communities (but, in fact do not have crime- however, DPD instead uses filler categories to say otherwise) or areas that DPD does not have constant surveillance. Partnerships with Amazon make it easy for police to add surveillance and have property owners self surveil. The normalization of surveillance and the heightened language around crime does influence and dictate our current policing state. If the DPD is using data as they want which is visible in their crime reports- can you imagine how they are using the data against us as well as the data collected from Amazon Ring? We already see police using data from Amazon Ring in criminal cases that blurs the line between factual data and reinterpreted data. There must be a level of transparency from Amazon Ring and DPD that shows how our data is used, collected, stored and disseminated. As police continue to partner with Amazon Ring, Amazon Ring will continue to hide behind the privatization of the company to not expose the collection and misuse of data.
This raises a more important issue of what "crime" or "property crime" actually means and to whom. As the graphs portray, DPD property crime and heat map crime are based upon a plethora of categories and padded categories that make up an overall property crime score. When in fact, my graphs focus on a sharpened viewpoint. To understand the true property crime within Dallas Districts, I only included categories that are directly related to residential property offenses. By fine tuning the data sets, I better represent a more truthful understanding of crimes within a residential area. As seen with the logic behind the disappropriate deviations of 'safe' versus 'dangerous' neighborhoods- the language of crime also threatens to reify existing racial narratives about neighborhoods. These vast deflections in certain neighborhoods is also exemplified in my graphs that include minority areas in comparison to income and property crime. Looking at our past, we know that crime reports typically reflect rates of police activity. This in turn, means that those targeted areas by police does not fully reflect crime, but instead excessive control over policing a certain area based upon minority demographics. These are not sites of danger and crime, but instead are areas that are hyper focused by police to frighten the public with a demystifying political language of crime to add additional surveillance or policing. We see this exemplified through the redistricting and gerrymandering of new districts within Dallas as visualized in the redistricting graphs above. As a result, this raises concern over how data is collected and used, as well as, the areas that are flagged as dangerous hotspot zones to push for more surveillance and additional funding to police.
In the Q&A section of the site, I propose additional questions of analysis to spur further discussion.
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