Could Data from Location-Based Social Networks Be Used to Support Urban Planning?

Smarzaro, Rodrigo; Lima, Tiago França Melo de; Davis, Clodoveu A.

doi:10.1145/3041021.3051700

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…Regarding QoL indicators, the rich literature, e.g., [44,45] rarely used location-based networks [46] or geosocial media. One of the opportunities of geosocial media is to find information on perceived and real problems, supporting small-area estimations and enriching traditional large-area statistics and labor-intensive surveys such as [47].…”

Section: Related Workmentioning

confidence: 99%

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Ostermann

2021

IJGI

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Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life.

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Section: Related Workmentioning

confidence: 99%

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Ostermann

2021

IJGI

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“…For instance, one application may represent a given phenomena as simple regions saved in a textual format, and another may represent it as complex regions saved in a binary format. A practical example of this situation is an application for integrating points of interest from public and private spatial data sources to support urban planning 62 . Another example is a system that integrates the geographic location of objects in an Internet of Things environment 63 .…”

Section: User‐centric Guidelinesmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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Summary Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.

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“…The intentions and experiences behind the data points are not in focus. Furthermore, these studies make the choice to aggregate data on predefined spatial units, such as blocks (De Nadai et al, 2016), land zones (García-Palomares et al, 2018), city regions (Smarzaro et al, 2017;, or counties . The methodological choice is to refrain from using any of the two affordances of data from the social web to move out of the two dimensions in our typology.…”

Section: Sensor Surrogates: Keeping New Data On the Beaten Pathmentioning

confidence: 99%

“…We found three other sensor surrogate papers in our review. De Nadai et al ( 2016) used social media activity to map activity in urban venues, and Smarzaro et al (2017) reduced online reviews to chart the availability of different types of services across the city. More surprisingly, also falls in this category.…”

Section: Sensor Surrogates: Keeping New Data On the Beaten Pathmentioning

confidence: 99%

“…To be fair, the possibility of alternative problem formulations is not at all the reason why these studies turned to the social web for empirical data in the first place. The reason is rather that the authors found established methods too "slow and expensive" (Smarzaro et al, 2017(Smarzaro et al, : 1463 or "hard to scale up" (Zhang et al, 2019: 2). In these studies, the fascination with social media traces is framed as the possibility of harvesting a lot of data quickly and cheaply.…”

Section: Scs: Exemplars Of An Emerging Research Agendamentioning

confidence: 99%

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Expanding Data Imaginaries in Urban Planning: Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

Thorsen

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Could Data from Location-Based Social Networks Be Used to Support Urban Planning?

Cited by 9 publications

References 20 publications

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Expanding Data Imaginaries in Urban Planning: Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

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