Mining point-of-interest data from social networks for urban land use classification and disaggregation

Jiang, Shan; Alves, Ana; Rodrigues, Filipe; Ferreira, Joseph; Pereira, Francisco C.

doi:10.1016/j.compenvurbsys.2014.12.001

Cited by 277 publications

(152 citation statements)

References 8 publications

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“…The quality assessment of VGI POIs is essential as many applications are based on such data, and can be affected by low-quality POIs. POIs from OSM, Wikipedia, or social networks such as Facebook or Swarm can be used for user-centric wayfinding [7], for population analysis [8,9], for land use mapping [10], for urban analysis [11], or for the analysis of people's perception of places [12].…”

Section: Introductionmentioning

confidence: 99%

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Touya¹,

Antoniou²,

Olteanu‐Raimond³

et al. 2017

IJGI

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Abstract:With the development of location-aware devices and the success and high use of Web 2.0 techniques, citizens are able to act as sensors by contributing geographic information. In this context, data quality is an important aspect that should be taken into account when using this source of data for different purposes. The goal of the paper is to analyze the quality of crowdsourced data and to study its evolution over time. We propose two types of approaches: (1) use the intrinsic characteristics of the crowdsourced datasets; or (2) evaluate crowdsourced Points of Interest (POIs) using external datasets (i.e., authoritative reference or other crowdsourced datasets), and two different methods for each approach. The potential of the combination of these approaches is then demonstrated, to overcome the limitations associated with each individual method. In this paper, we focus on POIs and places coming from the very successful crowdsourcing project: OpenStreetMap. The results show that the proposed approaches are complementary in assessing data quality. The positive results obtained for data matching show that the analysis of data quality through automatic data matching is possible but considerable effort and attention are needed for schema matching given the heterogeneity of OSM and the representation of authoritative datasets. For the features studied, it can be noted that change over time is sometimes due to disagreements between contributors, but in most cases the change improves the quality of the data.

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Section: Introductionmentioning

confidence: 99%

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Touya¹,

Antoniou²,

Olteanu‐Raimond³

et al. 2017

IJGI

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“…Specifically, TF-IDF is a method to evaluate how important a word is in the textual information. Jiang et al proposed a POI classification method with POI types for urban land use classification [18]. This method requires many labelled input data, which is also labour intensive and time consuming.…”

Section: Semantic Mining For Land Cover Classificationmentioning

confidence: 99%

“…It contains rich information about spatio-temporal data and textual messages, which provide an opportunity to understand environmental and social conditions [15]. Several researchers have applied many CGI resources including geo-tagged photos [8, 9,16], check-in data [17], POIs [6,18], OSM [10,11] and other CGI. CGI has been used in a variety of applications, such as environmental detection [19], disaster management [20], urban land use identification [21], and land cover validation [4,22].…”

Section: Land Cover Classification With Crowdsourced Geographic Datamentioning

confidence: 99%

Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

et al. 2017

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Abstract:Land cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information (CGI), including geo-tagged photos and other sources, has been widely used with lower costs, but still requires extensive labour for data classification. Alternatively, CGI textual information is available from online sources containing land cover information, and it provides a useful source for land cover classification. However, the major challenge of utilising CGI is its uneven spatial distributions in land cover regions, leading to less reliability of regions for land cover classification with sparsely distributed CGI. Moreover, classifying various unorganised CGI texts automatically in each land cover region is another challenge. This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification. Spatial clustering is employed for CGI to reduce the effect of uneven spatial distributions by extracting land cover regions with high density of CGI. To classify unorganised various CGI texts in each extracted region, land cover topics are calculated using topic model. As a case study, we applied this method using points of interest (POIs) as CGI to classify land cover in Shandong province. The classification result using our proposed method achieved an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential for land cover classification.

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“…The fields used in per-field urban land use classification have been gradually evolving from a traditional aggregated level (e.g., census tract or traffic analysis zone) to a more disaggregated level (e.g., census block or land parcel) [5], so that more detailed land use information can be derived. Smaller and more disaggregated fields are especially necessary for urban land use mapping in China, as the representative land parcel size is much smaller in China than in many other countries, such as Japan, Sweden, Canada, and the United States [26].…”

Section: Introductionmentioning

confidence: 99%

“…There has been increasing demand for fine-scale urban land use maps in the past several decades [5]. On the other hand, the timely acquisition of up-to-date land use information is of equal importance, because the urban environment has been changing at a greater pace, especially in rapidly developing regions [2].…”

Section: Introductionmentioning

confidence: 99%

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

et al. 2017

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Abstract:In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

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Mining point-of-interest data from social networks for urban land use classification and disaggregation

Cited by 277 publications

References 8 publications

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

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