The Role of Social Media Geographic Information (SMGI) in Spatial Planning

Campagna, Michele; Floris, Roberta; Massa, Pierangelo; Girsheva, A; Ivanov, Konstantin

doi:10.1007/978-3-319-18368-8_3

Cited by 28 publications

(27 citation statements)

References 19 publications

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“…Previous research has applied the DBSCAN algorithm to detect density clusters and extract activity centers for individuals from geo-tagged social media data [26,31]. Based on this research, we used DBSCAN to obtain each individual user's frequently visited areas.…”

Section: Extracting Activity Centersmentioning

confidence: 99%

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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Abstract:Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

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Section: Extracting Activity Centersmentioning

confidence: 99%

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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“…(PIC!) (Campagna, Floris, Massa, Girsheva, & Ivanov, 2015) Nonetheless, there are disadvantages to using VGI in planning. Partly, this comes back to the issue of participant motivation, which in the context of planning, likely requires willingness to be coincident on two fronts: to contribute to the production of VGI, and to contribute to the planning process (Obermeyer, 2007).…”

Section: Volunteered Geographic Information In Urban Planningmentioning

confidence: 99%

Leveraging VGI Integrated with 3D Spatial Technology to Support Urban Intensification in Melbourne, Australia

Sabri

Rajabifard

et al. 2016

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High density residential development in metropolitan Melbourne, where contradictory imperatives of neighbourhood character and urban intensification play important roles, remains an uncertain practice. One key issue for plan implementation is the lack of consistency between authorities, developers and the community in interpreting the standards, design guidelines, and state/local strategies, especially those relating to neighbourhood character. There is currently no mechanism to incorporate community perceptions and place experiences as subjective aspects of neighbourhood character in development assessments. There is also little use of micro-scale and multi-dimensional spatial analysis to integrate these subjective aspects with objective measures (e.g. building volume and height; streetscape) to communicate effectively-and in a limited timeframe-with all stakeholders. This paper explores the potential of two emerging geospatial technologies that can be leveraged to respond to these problems. Evidence in the literature suggests that volunteered geographic information (VGI) can provide community input around subjective aspects of the urban environment. In addition, a deluge of three-dimensional (3D) spatial information (e.g. 3D city models) is increasingly available for micro-level (building-or property-level) assessment of the physical aspects of the urban environment. This paper formulates and discusses a conceptual framework to link these two spatial technological advancements in a virtual geographic environment (VGE) that accounts for micro-scale 3D spatial analysis incorporating both subjective and objective aspects of neighbourhood character relevant in implementing compact city strategies.

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“…Successful empirical studies of using geosocial media data for gauging public perceptions of disaster response, identifying events, and investigating human activities have also been documented [44][45][46]. In addition to the primary focus of mining spatial patterns, others suggested the need to further incorporate qualitative social media content [13]. Afzalan and Muller [47], for example, found that informative dialogues are developed within online groups regarding local green infrastructure planning issues.…”

Section: Use Of Geosocial Media In Local Governmentsmentioning

confidence: 99%

“…Gal-Tzur et al [11], similarly, suggested that useful information about transportation policy can be harvested from social media. The combination of qualitative and spatiotemporal analysis may, as suggested by Campagna [13], provide more insights into the geographies of public needs.…”

Section: Use Of Geosocial Media In Local Governmentsmentioning

confidence: 99%

“…However, unlike citizen surveys or interviews, social media data are the outputs of users' communication. Hence, these data are unstructured, vary in quality, and often of unknown relevance to local governments' need [13]. Further complications arise from the fact that only a small portion of social media are tagged with explicit geographic coordinates and that these data vary widely in their geographic representativeness within and across urban areas [14].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Public Opinions from Geosocial Media

Zhang

Feick

2016

IJGI

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Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments' needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens' transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens' concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment.

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The Role of Social Media Geographic Information (SMGI) in Spatial Planning

Cited by 28 publications

References 19 publications

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Leveraging VGI Integrated with 3D Spatial Technology to Support Urban Intensification in Melbourne, Australia

Understanding Public Opinions from Geosocial Media

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