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![]() Cambridge Journal of Regions, Economy and Society, 8(1), 79–92. Smart cities: Moving beyond urban cybernetics to tackle wicked problems. Applying the general transit feed specification (GTFS) to the global south: Experiences in Mexico City and beyond. Annals of the Association of American Geographers, 102(3), 571–590.Įros, E., Mehndiratta, S., Zegras, C., Webb, K., Ochoa, M. Researching volunteered geographic information: Spatial data, geographic research, and new social practice. The Professional Geographer, 58(2), 197–208.Įlwood, S., Goodchild, M. Negotiating knowledge production: The everyday inclusions, exclusions, and contradictions of participatory GIS research. White knights of spatial data infrastructure: The role and motivation of key individuals. Transportation Research Record: Journal of the Transportation Research Board.Ĭraig, W. A user-flocksourced bus experiment in Dhaka: New data collection technique with smartphones. Intelligent Transportation Systems, IEEE Transactions on, 13(3), 1430–1441.Ĭhing, A., Zegras, C., Kennedy, S., Mamun, M. Traffic flow estimation models using cellular phone data. This process is experimental and the keywords may be updated as the learning algorithm improves.Ĭaceres, N., Romero, L. These keywords were added by machine and not by the authors. The lessons learned in Nairobi can be translated to other areas with the potential to use mobile applications to develop data on essential urban infrastructure and to extend the use of that data by sharing it with a larger community. We argue that one of the most important components of our work in Nairobi was the engagement process that created trust in the data and knowledge of its existence for the development of civic technologies. ![]() Citizens can then leverage open source tools made for that standard, enhancing access to information about the transit system. Through our work in Nairobi, this paper shows that cell-phone technology that is ubiquitous in most countries can be used to generate a dataset in an open standard, GTFS. Perhaps more importantly, this data can provide the ability to generate citizen-based information tools, such as transit routing applications for mobile devices widely discussed in the smart cities dialog (Townsend in Re-Programming mobility: the digital transformation of transportation in the United States, 2014). Cities that rely on these bus systems can benefit from the generation of digital data on these systems for planning and passenger information purposes. However, little to no digital information is typically available on routes, bus stops, passenger boarding, service frequency or scheduled trip times. For many cities in the developing world, public transit consists mainly of semi-formal mini-buses (paratransit).
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