Abstract. This paper presents a novel web-based platform that supports the analysis, integration, and visualization of large-scale and heterogeneous urban data, with application to city planning and decision-making. Motivated by the non-scalable character of conventional urban analytics methods, as well as by the interoperability challenges present in contemporary data silos, the illustrated system -coined SocialGlass -leverages the combined potential of diverse urban data sources. These include sensor and social media streams (Twitter, Instagram, Foursquare), publicly available municipal records, and resources from knowledge repositories. Through data science, semantic integration, and crowdsourcing techniques the platform enables the mapping of demographic information, human movement patterns, place popularity, traffic conditions, as well as citizens' and visitors' opinions and preferences about specific venues in a city. The paper further demonstrates an implemented prototype of the platform and its deployment in real-world use cases for monitoring, analyzing, and assessing city-scale events.
Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for generating crowdsourced object annotations from street-level imagery, in combination with object density and geo-location estimation techniques to enable the enumeration and geo-tagging of urban objects. To address both the coverage and precision of the mapped objects within budget constraints, we design a scheduling strategy for micro-task prioritization, aggregation, and assignment to crowd workers. We experimentally demonstrate the feasibility of our approach through a use case pertaining to the mapping of street trees in New York City and Amsterdam. We show that anonymous crowds can achieve high recall (up to 80%) and precision (up to 68%), with geo-location precision of approximately 3m. We also show that similar performance could be achieved at city scale, possibly with stringent budget constraints. CCS CONCEPTS • Information systems → Geographic information systems; Crowdsourcing; Web interfaces; • Human-centered computing → Collaborative and social computing systems and tools.
In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.
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