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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, Airb...
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Citations
... Over the last few years, LBSNs have become popular in the form of social media (Abesinghe et al. 2023;Bernabeu-Bautista et al. 2021). They provide services that are relevant to the location and allow users to check in at different geographical locations and thereby share experiences and visited places with their friends. ...
This paper presents a novel reverse geocoding approach that integrates multidimensional weather data into spatial reasoning, providing a context-aware and behaviorally driven alternative to existing solutions. By emphasizing the point of intersection between environmental factors and human mobility, it proposes a framework for enhancing reverse geocoding by modeling check-in behavior influenced by weather patterns. The key innovation lies in incorporating environmental conditions-specifically air temperature, humidity, wind speed, and cloudiness-into the geocoding process to model how weather influences human check-in behavior. While conventional methods rely primarily on spatial proximity, they often ignore such behavioral contexts. In our approach, 1 year of Swarm check-in data from California and New York were used to create weather semantic signatures for 599 venue categories, representing check-in likelihood under different weather conditions. These signatures were integrated into several mathematical distortion models to adjust spatial relationships, with the linear model showing the highest accuracy. To further enhance the model, Particle Swarm Optimization (PSO) was applied to determine optimal parameter weights. The proposed method was evaluated on unseen temporal data to assess the model's generalizability. The Mean Reciprocal Rank (MRR) improved by 14.90% in California and 13.40% in New York, while PSO optimization raised these to 18.48% and 15.37%, respectively. The First Position index also improved significantly by 76.43% and 83.04%. These findings highlight the effectiveness of the proposed method for enhancing reverse geocoding for urban applications.
... Rabiei-Dastjerdi et al. [35] analyzed Google reviews in Dublin to map cultural diversity, demonstrating the potential of user-generated data in cultural studies. The importance of such data in urban research is further emphasized by studies such as that of Bernabeu-Bautista et al. [36], which highlighted the relationship between economic activity areas and location-based social network data. The ongoing research continues to explore these relationships, offering new insights into the spatial dimensions of visitor experiences [11,32,37]. ...
In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making.
... This probability is due to the dynamics of social media usage and digital coverage variability. Bernabeu-Bautista et al. (2021) demonstrated that urban areas exhibit higher online activity compared to mountain regions, while Sapienza et al. (2023) further documented diverse social media behaviors across various geographic areas. Therefore, to effectively model the SIR models, we filtered out cities where tweets did not occur continuously or where only a few tweets were posted per day. ...
The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses social media data to assess public responses and explores how these responses are linked to city-level community characteristics. Specifically, we leveraged a transformer-based topic modeling technique called BERTopic to identify wildfire response-related topics and then utilized the Susceptible-Infectious-Recovered (SIR) model to compute two key metrics associated with wildfire responses - awareness and resilience indicators. Additionally, we used GIS-based spatial analysis to map wildfire locations along with four groups of city-level factors (racial/ethnic, socioeconomic, demographic, and wildfire-specific). Our findings reveal significant geographic and socio-spatial differences in public responses. Southern California cities with larger Hispanic populations demonstrate higher wildfire awareness and resilience. In contrast, urbanized regions in Central and Northern California exhibit lower awareness levels. Furthermore, resilience is negatively correlated with unemployment rates, particularly in southern regions where higher unemployment aligns with reduced resilience. These findings highlight the need for targeted and equitable wildfire management strategies to improve the adaptive capacity of WUI communities.
... Younger individuals are overrepresented, skewing the age distribution and influencing the content and trends observed. Additionally, the geographic distribution of users is uneven, with urban areas, particularly major cities, being overrepresented compared with rural regions [74]. This creates a biased view of public opinion and behavior. ...
Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.
... With rapid advances in information technology and the widespread use of smartphones, VSAs have grown in popularity. This has made it possible for voluntary geographic information (VGI), or unprocessed data based on contributions from social media users, to be widely used for collective social networking [15,16], behavioral trends [17,18], reinterpretation of urban structure [19,20], spatio-temporal population movements [21], etc. Despite the claim of the "death of geography", VGI demonstrates that OSN is correlated with the spatial distribution of economic activity density [19], population density, and public services per capita [12], as well as the fact that the linkage of OSN is also inseparable from ISPRS Int. ...
... This has made it possible for voluntary geographic information (VGI), or unprocessed data based on contributions from social media users, to be widely used for collective social networking [15,16], behavioral trends [17,18], reinterpretation of urban structure [19,20], spatio-temporal population movements [21], etc. Despite the claim of the "death of geography", VGI demonstrates that OSN is correlated with the spatial distribution of economic activity density [19], population density, and public services per capita [12], as well as the fact that the linkage of OSN is also inseparable from ISPRS Int. J. Geo-Inf. ...
High-speed information technology development has made virtual social networking (VSN) a social interaction trend. Studies have been carried out to investigate the spatial clustering characteristics of the locations where there is online social interaction, but they have not yet concentrated on the geographic phenomenon associated with the distribution of occupational and residential locations of citizens who use VSN. According to usage statistics gathered from China Unicom for people living in the Wuhan metropolitan development area, there are geographical characteristics for the sites of employment and residence of virtual social application (VSA) users. Compared with people who live or work in the central city, suburban citizens are more willing to conduct virtual social networking, and those who are most likely to do so are concentrated in the suburbs 20–30 km from the main city. Additionally, we used geographically weighted regressions to evaluate the relationship between the density of physical social facilities and the possibility of the usage of VSAs, revealing the influence of various conventional social conveniences on the propensity to use the VSA. Residents are more inclined to engage in VSN in places where traditional social interaction is inconvenient, particularly in suburbs, indicating that VSN is an addition to traditional social interaction. Nonetheless, neither an improvement in, nor the replacement of, VSN activities is apparent in places where conventional socializing is practical. This study identified the clustering of virtual social users’ places of employment and residence in metropolitan areas and concluded that virtual social interaction offers new social channels for people who lack access to adequate physical social facilities; that is, it complements traditional social interaction. These results can deepen the understanding of the relationship between traditional social interaction and VSN. They also offer a fresh viewpoint on facility planning for the potential future creation of a more balanced and diverse social interaction environment through the joint planning of virtual and physical social facilities.
... The database consulted in this article is insufficient to address all these questions, so it would be interesting to combine these results with the exploration of other tools used in previous works, such as Google Places , Foursquare Bernabeu-Bautista et al., 2021) or Twitter (Huang et al., 2022), among other things, this could further contribute to understanding post-COVID supply and demand for short-term rentals. ...
The COVID-19 pandemic has had a significant impact on the tourism industry worldwide. This study aims to understand the early effects of the pandemic on tourist supply and demand by analysing Airbnb's occupancy rates and accommodation prices at both city and neighborhood/district scales in Madrid and Valencia, two Spanish destinations with distinct tourism types. By considering both spatial and statistical analyses at different scales, this study provides valuable insights into the short-term impacts of COVID-19 on the tourism industry in these destinations. The findings reveal a spatial polarisation, with certain areas maintaining higher occupancy rates and prices, suggesting resilience to the crisis, particularly those near green spaces. The analysis further highlights varied effects of the pandemic across different months and neighborhoods/districts. While historic center neighborhoods experienced declines in both occupancy rates and prices, districts with a stronger tourist tradition showed greater resilience. Price rigidity is observed in some urban areas, where occupancy rates decline while prices remain relatively stable or even increase. Two key recommendations are underscored for decision makers: (1) regulating Airbnb should consider neighborhood-specific characteristics and differentiate between types of tourism, establishing minimum standards for housing conditions and the surrounding environment and (2) touristic cities should aim for a polycentric spatial structure by expanding and diversifying tourist areas, avoiding concentration in a single location.
... To clearly visualize hot streets, the approach is to try to remove locally dense areas and over-scattering in each trajectory by rearranging the reference points of a trajectory so they are exactly enough to represent the areas where the users actually walked and stayed. With the spread of smartphones, it has become natural for tourists to take photos and post comments on social networking services [37,38]. We assumed that recoding UGC would originate from tourist interest and decided to include the location data tagged as ugc in the trajectory data. ...
This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the semantic importance of hot spots and the presence of street-level features on a density map. Our mobile collaborative framework reorganizes tourist trajectories. This processing comprises (1) extracting the location of the user-generated content (UGC) recording, (2) abstracting the locations where tourists stay, (3) discarding locations where users remain stationary, and (4) simplifying the remaining points of location. Then, our heatmapping system visualizes heatmaps for hot streets, UGC-oriented hot spots, and indoor-oriented hot spots. According to our experimental study, this method can generate a trajectory that is more adaptable for hot street visualization than the raw trajectory and a simplified trajectory according to its geometry. This paper extends our previous work at the 2022 IEEE International Conference on Big Data, providing deeper discussions on application for local tourism. The framework allows us to derive insights for the development of guide content from mobile sensor data.
... These home locations allow the geographic distribution of the sample of MP users to be compared to 'ground truth' sources such as official population statistics (Berke et al., 2022;Calabrese et al., 2013;Huang et al., 2022;Mao et al., 2015;Phithakkitnukoon et al., 2012;Wang et al., 2019;Çolak et al., 2015). This process provides a very useful measure of differential geographic coverage (Yabe et al., 2020) as well as variations by the socio-demographic status of different areas (Bernabeu-Bautista et al., 2021;Huang et al., 2020Huang et al., , 2020Huang et al., , 2022Huang et al., , 2020. Enriching the data in this way also greatly increases the potential impact of research. ...
... but this increases disclosure risks. The best approach therefore appears to be exploration of spatial coverage on the basis of estimated home locations with comparison to 'ground truth' sources such as official population statistics (Berke et al., 2022;Huang et al., 2022;Wang et al., 2019) as well as area-level measures of socio-demographic status (Bernabeu-Bautista et al., 2021;Huang et al., 2020). ...
Emerging forms of mobile phone data generated from the use of mobile phone applications have the potential to advance scientific research across a range of disciplines. However, there are risks regarding uncertainties in the socio-demographic representativeness of these data, which may introduce bias and mislead policy recommendations. This paper addresses the issue directly by developing a novel approach to assessing socio-demographic representativeness, demonstrating this with two large independent mobile phone application datasets, Huq and Tamoco, each with three years data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. We advance methods for detecting home location by including high-resolution land use data in the process and test representativeness across multiple dimensions. Our findings offer greater confidence in using mobile phone app data for research and planning. Both datasets show good representativeness compared to the known population distribution. Indeed, they achieve better population coverage than the 'gold standard' random sample survey which is the alternative source of data on population mobility in this region. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future.
... However, this is problematic in two ways. First, user generated content or what Bernabeu-Bautista, Serrano-Estrada [25] call "volunteered generated information (VGI)" is affected by the platform on which it is hosted. For example, as Zasina [26] argues, Instagram content does not reflect the urban space in general. ...
... In other words, it neglects the agency of both users and data. By sharing data which they both receive and produce [25] social media users affect the performance of buildings, they co-create meaning and symbolism of buildings and institutions and according to Iglesias-Sánchez, Correia [28] even a city's image or brand. Tourism studies acknowledge that social media should be considered not only as a communication tool, but also as an active component in the destination's image [28]. ...
Exceptional public buildings are buildings that are commissioned by public institutions with the deliberate and declared intention that they become icons. The recognisability of such buildings and their exposure to a wide audience can support the interests of their producers, who are keen on developing symbolic landmarks of their institutions. Textual and visual online communication can play an important role in boosting exposure and affecting how a building acquires iconic status. Content produced by social media users not only reflects how such buildings are perceived, it also goes on to affect how they perform, that is, what narratives they become associated with and how such association supports their transformation into icons. However, the role of content produced by different actors is unclear. In particular, what differences, similarities and influences exist between public/supply and private/user generated social media content particularly during the early life cycle of a building. This article presents a research methodology that can address these questions. Findings generated by applying this methodology on the case study of Depot Boijmans in the City of Rotterdam are presented. By scraping, filtering, organising and analysing content produced by Instagram users about the case study it was possible to show that that public/supply and private/user generated content converge independently. Textual analysis of posts uncovers an overwhelmingly neutral and positive sentiment in posts. Newness, iconicity and the novelty/firstness of the publicly- accessible- art- storage-concept emerge as central topics and are discussed in equal quantities on both the public and private actors. Post behaviour is characteristically different amongst actors, where public actors tell longer stories than private actors but both coinciding with events. The clustering of photographs of the building reveals a trend towards more than one point of interest. The results demonstrate that the exceptional building currently receives more attention on Instagram than the valuable masterpieces stored within it. This suggests the intended performance is achieved in the short term but its long-lasting effects and its assimilation to become an icon in the Museum Park or even the City of Rotterdam will be tested with time. It suggests the photograph itself becomes an actor in the creation of its iconic status in visual media platforms and recognises the agency of non-persons, and that Instagram is merely one of many (social) media platforms used to do so. This research offers methods and their applicability toward a catalogue for data analysis for architecture and urban studies related to the online performance of buildings.
... Beyond its economic advantages, bank cards may be socially meaningful and the data that they record a complementary means to overcome the "largely descriptive and all too often simplistic mapping of store location, location, location" (Crewe, 2000, p. 275), common in economic geography studies. For this purpose, expenditure data mapping could be compared with other spatial analysis focusing on the socio-symbolic dimension of commercial spaces (their social meaning and prestige), which has started to be analyzed at new scales thanks to new data sources like social media (Bernabeu-Bautista et al., 2021;Carpio-Pinedo & Gutiérrez, 2020). ...
The spatial distribution of commercial activities is vital to support healthy lifestyles and to achieve livable public spaces and environmental, social and economic sustainability in our cities. However, commercial activities require a constant flow of expenditure for their own viability. As a result, understanding the spatial and temporal distribution of expenditure is fundamental, although the lack of detailed, complete data sources has impeded this task until now.
Bank card data paves the way for a new urban geography of expenditure, thanks to its fine spatial and temporal granularity along with the uniform coverage of all commercial sectors. In this paper, we analyze temporal, spatial, and spatiotemporal distributions of expenditure at the intraurban scale of the city of Madrid (Spain), combining spatial statistical tools (Getis-Ord General for global autocorrelation and Getis-Ord Gi* hot spot analysis for local autocorrelation) with k-means cluster analysis and spatiotemporal tools (Time Series Clustering analysis and Temporal Hot Spot Analysis).
Our analysis confirms the strong center-periphery gradient described in previous literature, but with a CBD integrated by distinct specialized areas. The paper demonstrates that bank card data has a great potential to support a new geography of expenditure that could strengthen decision-making in planning and retailing.
