Where to go and what to do: Extracting leisure activity potentials from Web data on urban space

Weerdenburg, Demi van; Scheider, Simon; Adams, Benjamin; Spierings, Bas; Zee, Egbert van der

doi:10.1016/j.compenvurbsys.2018.09.005

Cited by 35 publications

(24 citation statements)

References 41 publications

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“…This source is used as an additional layer of information to support the recognition of Third Places, taking the assumption that tweet location and concentration patterns suggest, to a degree, which urban areas have more or less people presence. Based on the previous considerations and cited literature (Martí et al 2019;Serrano-Estrada et al 2016;Van Weerdenburg et al 2019), this study considers Google Places places as the urban activity "on offer", that is, a listing that includes all the economic activities in a neighbourhood; Foursquare venues as urban activities "on demand", that is, listings containing the urban spaces and establishments that have received, at least, one user check-in for broadcasting her/his presence in the space; and, the geolocated tweets, as indicative of people presence in a given urban area.…”

Section: Data Collection and Sourcesmentioning

confidence: 99%

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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This research focuses on the intermediate city, composed of urban areas located right outside the city center typically maintaining an in-between urban/suburban character. It aims to explore the degree to which this segment of the city exhibits urban activity and social life through the identification of activity areas in the so-called Third Places. Four intermediate city neighborhoods in Gothenburg, Sweden are adopted as case areas and are analyzed using a twofold approach. First, socioeconomic statistics provide a quantitative understanding of the case areas and, second, geolocated Social Media Data (SMD) from Foursquare, Google Places and Twitter makes it possible to identify the intermediate city's urban activity areas and socially preferred urban spaces. The findings suggest that a) the four analyzed intermediate city areas of Gothenburg all have a degree of social activity, especially where economic activities are clustered together; b) Third Places in more affluent areas tend to be linked to commodified consumption of urban space while neighborhoods with lower income levels and higher ethnic diversity seem to emphasize open public space as Third Places; and c) nowadays the typology of Third Places has evolved from the types identified in previous decades to include additional types of places, such as those you pass on the way to something else (e.g. gas and bus stations). The study has verified the value of SMD for studies of urban social life but also identified a number of topics for further research. Additional sources of SMD should be identified to secure a just representation of Third Places across diverse social groups. Furthermore, new methods for effective cross validation of SMD with other types of data are crucial, including e.g. statistics, on-site observations and surveys/interviews, not least to identify Third Places that are not frequently present (or are misrepresented) in SMD.

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Section: Data Collection and Sourcesmentioning

confidence: 99%

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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“…However, due to the lack of an effective data source, the UNLS distribution is sometimes difficult to acquire at the regional scale. In addition, social census data always suffer from coarse temporal and spatial resolutions because data on basic districts or areas of arbitrary shape are inaccessible in many countries [23]. Therefore, conducting UNLS mapping at different spatial resolutions using geographical big data as an alternative to traditional social census data is an important and challenging task for the academic community [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…There was an expressed need for larger areas of open space and parkland and better facilities for a wider range of age groups. (2) Based on network geographic data, Demi et al [23] proposed a method to determine the potential of leisure activities from urban spatial network data by using the semantic theme model. Taking the city of Zwolle as an example, network text data and geographical location tags were used to estimate the different types of leisure activities.…”

Section: Introductionmentioning

confidence: 99%

Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data

et al. 2020

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Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km2, which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures.

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“…With case studies using Yelp data for four U.S. cities, they showed that some POI categories have more unique surroundings than others. Van Weerdenburg et al [22] also proposed a method to extract leisure activity potentials from web data on urban space using semantic topic models. Finally, based on geolocated webtexts and place tags, three supervised multi-label machine learning strategies were tested to estimate whether a given type of leisure activity is afforded or not.…”

Section: Introductionmentioning

confidence: 99%

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

2020

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Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.

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Where to go and what to do: Extracting leisure activity potentials from Web data on urban space

Cited by 35 publications

References 41 publications

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

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