Investigating “Locality” of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

Sun, Yeran

doi:10.3390/ijgi5040043

Cited by 13 publications

(7 citation statements)

References 48 publications

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“…Semantic information from POIs has been used in a myriad of studies and applications, such as mobile POI recommendation [10,11], spatial analyses of socio-economic processes [12,13], land-use estimation from individual buildings [14,15], grid cells [16] and urban parcels [17,18], neighbourhood vibrancy description [19], semantic enrichment of streets segments [19,20], urban mobility modelling [21,22] and pedestrian navigation [23,24], to name a few. These and other studies and applications can benefit a lot from the conflation of POI semantic information dispersed across different VGI sources.…”

Section: Steps In the Matching Of Pois From Different Datasetsmentioning

confidence: 99%

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Novack

Peters

Zipf

2018

IJGI

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Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a "naive", though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method.

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Section: Steps In the Matching Of Pois From Different Datasetsmentioning

confidence: 99%

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Novack

Peters

Zipf

2018

IJGI

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“…Moreover, many studies have proved that human movement is not a random process (Gonzalez, M. C. et al 2008), and different groups of users exhibit different characteristics on LBSN data. For example, the "locality of social media behaviors" has been mentioned in several pieces of research (Sun, Y. 2016;Yin Zhihong, 2014;Jue, J., & Xiaolu, G. 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, most researches either only focus on the tourists (Vu, Huy Quan et al 2015;Kádár, B. 2014) or the residents (Sun, Y. 2016).…”

Section: Introductionmentioning

confidence: 99%

Identifying the urban space for locals and tourists through “Foursquare” data in Barcelona

Yang¹,

Duarte²,

Ciriquián³

2018

Libro De Proceedings, CTV 2018: XII Congreso Internacional Ciudad Y Territorio Virtual: “Ciudades Y Territorios Inteligentes”:

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Barcelona is an important touristic city in the world. According to Annual Report of Tourism of Barcelona (2014), more than 7.5 million tourists visited here in that year. The studies related to tourism of Barcelona are numerous; however, the comparison of activities and land uses between tourists and locals is scarcely analyzed. In fact, tourism may be a dominant factor of urban development as well as a source of social conflict. Therefore, it is crucial to understand the co-living situation of tourists and residents in a touristic city. The main objective of the study is to identify touristic users and local users through their Foursquare behaviors. Furthermore, it explores the difference of geospatial activities and POIs' usages between the two groups. The analytical period is from April of 2012 to September of 2013, based on the monitoring span of Foursquare data. After filtration, the total check-ins during this period is 80,936 coming from 4,250 Foursquare users. The POIs of Foursquare are 13,887 in Barcelona. The geographic range of data roughly covers the central conurbation of the Metropolitan area of Barcelona.

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“…With social media data, such as sign-in data, which are frequently obtained in the point of interest (POI) of a city, POI locations and their heat level can also be obtained through social media data. Researchers have realized that social media data could effectively reflect people's daily activities [5][6][7][8]. Although studies have started to use social media data to determine the new commercial centers of a city or the influential areas of commercial facilities, the research scale remained macroscopic.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network

2018

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The Chinese retail industry is expected to grow dramatically over the next few years, owing to the rapid increase in purchasing power of Chinese consumers. Retail managers should analyze the market demands and avoid dull sales to promote the sustainable development of the retail industry. Economic sustainability in the retail industry, which refers to a suitable return of investment, requires the implementation of precise product allocation strategies in different regions. This study proposed a hybrid model to evaluate economic sustainability in the preparation of goods of retail shops on the basis of market demand evaluation. Through a grid-based convolutional neural network, a regression model was first established to model the relationship between consumer distribution and the potential market demand. Then, another model was proposed to evaluate the sustainability among regions based on their supply-demand analysis. An experiment was conducted based on the actual sales data of retail shops in Guiyang, China. Results showed an immense diversity of sustainability in the entire city and three classes of regions were distinguished, namely, high, moderate, and limited. Our model was proven to be effective in the sustainability evaluation of supply and demand in the retail industry after validation showed that its accuracy reached 92.8%.

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Investigating “Locality” of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

Cited by 13 publications

References 48 publications

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets

Identifying the urban space for locals and tourists through “Foursquare” data in Barcelona

Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network

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