Matthew Hall
Is open data good because it makes us feel better or because it makes us live better? Are we motivated to make government spending more transparent because we feel that it is morally right to open data to citizens or are we motivated by the belief that greater transparency and participation will improve governance? Answering this question is important because it informs the design of open government tools and processes. Many of the recent attempts at making government more transparent by offering citizens a digital look into government’s balance sheets ultimately fail to engage citizens with meaningful data. Open data programs such as, Washington D.C.’s budgeting dashboard, offer citizens a disorienting view of all of the city’s spending data. My question is how are citizens supposed to know whether or not the numbers they are looking at are good or bad, not whether they are accurate but whether they signify successful programs. Also it is very difficult for a citizen to look up personally relevant information. Meaning, if they want to know how much the city is spending on their local park, they are out of luck because they can only get data for all city parks. These types of tools send the message that data should be accessible but not usable. If citizens had usable data that would actually inform them of the financial state of their community services then government would benefit from the collective intelligence of thousands upon thousands of monitors constantly reporting on the state of those services.
Location based budgeting data gives citizens uniquely relevant data that changes based on their current location, therefore, giving them a tool for active monitoring and participation. It also curates data based on users’ check-ins and preferences, so that they can monitor only what is important to them. Filtering spending data based on interest and location engages citizens and take advantage of the benefits of modularity within collective intelligence.
Modularity means splitting up a large task into smaller pieces, so they can be managed easier, and then reassembling them once they are finished into a massive solution. The task of monitoring all of a city’s data is truly massive, so the modularity of filtering data based on an individuals interests and location breaks down the task into a manageable amount and, since every individual will have slightly different interests and locations, all of the city’s data can be managed. Instead of trying to enlist citizens to mostly monitor data that is not even relevant or interesting to them, filtered data asks them to monitor what is most interesting to them making it more likely that they will be engaged and continuously participate.
Location based budgeting data engages citizens by giving them a usable tool to actively participate in governing their community.
