2018
DOI: 10.1080/01944363.2018.1476174
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Crowdsourcing Bike Share Station Locations

Abstract: Problem, Research Strategy, and Findings: Planners increasingly involve stakeholders in co-producing vital planning information by crowdsourcing data using online map-based commenting platforms. Few studies, however, investigate the role and impact of such online platforms on planning outcomes. We evaluate the impact of participant input via a public participation geographic information system, PPGIS, a platform to suggest placement of new bike share stations in New York City and Chicago. We conducted two anal… Show more

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Cited by 31 publications
(11 citation statements)
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“…Five main criteria of demographic variables, accessibility, the attractiveness of the shopping district, the potential for development, and competitiveness (designed by planners compared with suggested by people) were considered. Griffin and Jiao [37] examined the difference between station location proposals with a higher detail level from planners and citizens. The study evaluated the impact of participant input via a public participation geographic information system (PPGIS) to suggest the location of new BSS stations in New York City and Chicago.…”
Section: Operation Improvement Problemsmentioning
confidence: 99%
“…Five main criteria of demographic variables, accessibility, the attractiveness of the shopping district, the potential for development, and competitiveness (designed by planners compared with suggested by people) were considered. Griffin and Jiao [37] examined the difference between station location proposals with a higher detail level from planners and citizens. The study evaluated the impact of participant input via a public participation geographic information system (PPGIS) to suggest the location of new BSS stations in New York City and Chicago.…”
Section: Operation Improvement Problemsmentioning
confidence: 99%
“…Crowd-sourced geographical data (e.g., mobile check-in data, cellular signaling data, and taxi trajectory data) are rich in information, low cost, and abundant [14]. Such data have been widely used in sensing the geographical environment [15], recognizing urban structure and functional areas [16], planning urban development [17], assisting sustainable economic development [18], perceiving geographical events [19,20], and crowdmapping [21]. Therefore, social sensing based on crowd-sourced geographical data provides a practical approach to explore the spatial behavior of the public and reveal geographical features of the socioeconomy [22].…”
Section: Chinese Spring Festival Travelmentioning
confidence: 99%
“…In co-productive planning, emphasis is shifted from words to actions—the public can be responsible for generating the data necessary for planning decisions, in addition to performing other tasks alongside, or in place of state sponsorship ( 18 , 19 ). However, when digital technology is involved in co-productive processes such as crowdsourcing, varying levels of digital inclusion create an opportunity for bias that could further disparities by race, education, and income ( 20 22 ). Co-productive planning processes may support additional ways for people to guide their future communities, but integration of technologies must consider the role of distributional biases.…”
Section: Previous Researchmentioning
confidence: 99%