Parcels, points, and proximity: Can exhaustive sources of big data improve measurement in cities?

Abstract

While there has been no shortage of discussion of urban big data, smart cities, and cities as complex systems, there has been less discussion of the implications of big data as a source of individual data for planning and social science research. This study takes advantage of increasingly available land parcel and business establishment data to analyze how the measurement of proximity to urban services or amenities performed in many fields can be impacted by using these data—which can be considered “individual” when compared to aggregated origins or destinations. We use business establishment data across five distinctive US cities: Long Beach, Irvine, and Moreno Valley in California; Milwaukee, Wisconsin; and the New York borough of Staten Island. In these case studies, we show how aggregation error, a previously recognized concern in using census-type data, can be minimized through careful choice of distance measures. Informed by these regions, we provide recommendations for researchers evaluating the potential risks of a measurement strategy that differs from the “gold standard” of network distance from individually measured, point-based origins and destinations. We find limited support for previous hypotheses regarding measurement error based on the abundance or clustering of urban services or amenities, though further research is merited. Importantly, these new data sources reveal vast differences across cities, underscoring how accurate proximity measurement necessitates a critical understanding of the nuances of the urban landscape under investigation as measures appear heavily influenced by a city’s street layouts and historical development trajectories.

Information Extraction and Data Planning of Smart City Based on Internet of Things

“…This layer provides basic equipment resources to the SaaS layer and PaaS of the system. This layer is also responsible for the management and healthy operation of each resource pool and physical devices and allocates and controls the capacity of various resource pools (2) The PaaS layer provides standardized shared cloud services to each application. This layer includes business operation and application incubation In the entire smart city management system, it is necessary to monitor the city's situation.…”

“…Secondly, in the traditional model, the information channel is single, and the accuracy of the data obtained is limited, which can no longer match the requirements of modern urban planning; the application of big data technology can quickly expand the information and sources of urban and rural planning data. Third, the current speed of informatization is further accelerated, and the usage of the Internet is further increased, which provides great convenience for the realization of big data [2].…”

What Role Do Urban Parks Play in Forming a Sense of Place? Lessons for Geodesign Using Social Media

“…These massive sources are now being used to gain insights into how we understand the city and human behavior patterns. An increasing number of publications have identified social media data with great potential for investigating when, where, who, and how people use urban open spaces [50,53,59,61,76]. However, this readily available and rich data stream could also be challenging due to its nature of also presenting some risks (see Section 5.3 below for a discussion on these challenges).…”

“…As of 2016, there are over 330 million monthly active users on Twitter [69]. These numbers have been increasing over the past decade and continue to rise steadily [53]. Also, crowdsourcing geoinformation on social media is biased towards urban perspectives [70,71], while they offer generalizable and comparable information in densely populated metropolitan areas [69,72].…”

“…The literature widely acknowledges the potential of big data in enhancing our understanding of the built environment [51][52][53]. Advanced information technologies have enabled the investigation of places with a wide range of new data sources including data from sensors, monitoring, remote sensing, cell phone tracking, and social media [48].…”