These are the following questions I will attempt to answer using Gephi<\/a> by creating a network analysis graph.
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After the death of George Floyd on May 25 of 2020, social unrest and protests proliferated all across the US and internationally. My social media feeds were permeating with history, information, updates, violence of police brutality in this country but also it being done globally. As community organizers and grass-root organizations began to share rallies and protests across social media, they also began to alert everyone to turn off their phones and dress discreetly because of drones and cell-site simulators. This was the first moment I consciously knew that I was one of many being targeted by police surveillance. <\/p>\n\n\n\n
Process<\/h2>\n\n\n\nData<\/h3>\n\n\n\n
The Electronic Frontier Foundation partnered with University of Nevada’s Reynolds School of Journalism on Atlas of Surveillance: Documenting Police Tech in our Communities with Open Source Research<\/a> project. The following features I was most interested were the following: <\/p>\n\n\n\n The original dimensions of the dataset were 26 features by 7,303 observations. I removed all observations where Agency<\/strong>, Vendor<\/strong> ,Technology<\/strong>, State<\/strong> were undefined. I uploaded my dataset into OpenRefine<\/a> to update the discrepancies of naming conventions to make sure they all coincided. <\/p>\n\n\n\n My initial goal was to use the features laid out above, but I experienced difficulty when setting my edge table in Gephi. Somehow, it duplicated my target<\/strong> column onto the nodes Id<\/strong> column, creating an insurmountable number of null values when beginning to graph. Because of this, I used the following video Convert Excel CSV into Network<\/a> in order to make my graph more understandable to me As the following visualization points out, the surveillance technologies registered in the dataset are as follows:<\/p>\n<\/div>\n<\/div>\n\n\n\n The network analysis shows the relationship between the above surveillance technologies with states in the U.S. I found it interesting takeaways were Puerto Rico and the US Virgin Islands are only utilizing gunshot detection surveillance. Florida has the highest use of facial recognition technology, Tennessee has a strong use of predictive policing technologies. <\/p>\n\n\n\nCleaning<\/h3>\n\n\n\n
Preparing<\/h3>\n\n\n\n
Edge Table<\/span>
Target<\/em> being Technology<\/strong>
Source<\/em> being the State<\/strong>
Type<\/em> being Undirected<\/strong>
Weight
Nodes Table<\/span>
Id<\/em> & Label<\/em> were State<\/strong> and Technology<\/strong>
Frequency<\/em>
Type<\/em>: Source<\/strong> or Target<\/strong>
<\/p>\n\n\n\nVisualization<\/h3>\n\n\n\n
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