Assessing the influence of point-of-interest features on the classification of place categories

Milias, Vasileios; Psyllidis, Achilleas

doi:10.1016/j.compenvurbsys.2021.101597

Cited by 30 publications

(18 citation statements)

References 15 publications

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“…However, it was proved that regardless of the socioeconomic status of an urban area, the location and density of economic activity-represented in this case by Google Places data-had a strong influence on where data from other sources were shared. This is probably the reason why several studies have found these specific social networks to be complementary for characterizing different urban phenomena [1,78,79].…”

Section: Discussionmentioning

confidence: 99%

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

Bernabeu-Bautista

Serrano-Estrada

Perez-Sanchez

et al. 2021

IJGI

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This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities.

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Section: Discussionmentioning

confidence: 99%

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

Bernabeu-Bautista

Serrano-Estrada

Perez-Sanchez

et al. 2021

IJGI

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“…Even though each of the selected social media platforms meet very specific purposes, different from that of this study, they were selected for several reasons: (i) they include geolocated user-generated information, which means that the users' contributions are associated with a specific geographical space; (ii) their data are rich in spatiotemporal content, which allows a characterization of the data in a specific time frame; (iii) they are representative of different types of social media sources [47], thereby offering diverse information from the same geographical context; and (iv) although they have different functionalities, the three sources have proven to be complementary to each other for both analysing and diagnosing temporal and socio-spatial urban dynamics [48][49][50].…”

Section: Digital Footprintsmentioning

confidence: 99%

Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints

2021

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Acknowledging and sustaining the ethnic and cultural diversity that the phenomenon of globalization has brought to the urban environment is one of the target objectives to seek spatial and social sustainability in today’s cities. This study aims to build upon previous research approaches that sought to characterize culturally diverse, urban public spaces. For this purpose, a method that encompasses digital and physical layers of information has been proposed to gather signs of culturally diverse, street-level urban and economic activities. Geolocated data from three social media platforms, as well as field-work observations, were collected from two case study street segments with different socio-cultural, demographic profiles. The findings suggest that economic activities related to retail and restaurants, and especially those with higher levels of personalization that reflect “cultural specialisms”, have a significative relevance in both the physical and virtual domains. However, unlike the case study area with the higher socio-economic profile, the urban vibrancy observed through field work throughout the more culturally diverse street segment was not found to be fully represented by social media data. There is still much room for research on the extent to which these sources are useful for characterizing fine-grain street-scale phenomena.

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“…Recent advances in computer science and the development of the Internet have led to large volumes of emerging geospatial data [21][22][23][24][25][26], allowing new solutions to the aforementioned challenges to be addressed. Strategic sites and vulnerable locations are increasingly becoming available in the form of points of interest (POI) dataset, which contains geographical entities such as schools, factories, supermarkets, etc.…”

Section: Introductionmentioning

confidence: 99%

Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing

Wang

Sun

et al. 2021

IJGI

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The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities.

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Assessing the influence of point-of-interest features on the classification of place categories

Cited by 30 publications

References 15 publications

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints

Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing

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