Functional urban land use recognition integrating multi-source geospatial data and cross-correlations

Zhang, Yatao; Li, Qingquan; Tu, Wei; Mai, Ke; Yao, Yao; Chen, Yiyong

doi:10.1016/j.compenvurbsys.2019.101374

Cited by 84 publications

(63 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…POI has become one of the important data in urban research [22]. At present, scholars find that POI performs very well in exploring the formation of urban spatial structure [23] and the identification of the urban functional area [24].…”

Section: Identification Data and Methods Of Urban Spatial Structurementioning

confidence: 99%

Research on the relationship between Urban economic development level and urban spatial structure—A case study of two Chinese cities

Zhang

Yuan

2020

PLoS ONE

View full text Add to dashboard Cite

The characteristics of urban spatial structure and the objective evaluation of the development level of urban economy have always been the concern of urban researchers, However, the spatial relationship between urban spatial structure and urban economic development level is often deliberately ignored. Through the point of interest (POI), the identification framework is constructed, the spatial structure of the city is identified and evaluated, and the Geographically Weighted Regression analysis is carried out with the distribution of unit GDP (Gross Domestic Product) in this study. The research shows that Kunming and Guiyang are polycentric spatial structures and Kunming's structure is more significant. Kunming's economic level is generally higher than Guiyang, but the unit area cannot be compared. The city center will promote the development of the central area in this city, and the more urban centers are distributed within the geographical and spatial range, the greater contribution would have to economic development. In addition, the results of this study will have a positive impact on urban planning and construction, and will also provide a new perspective for the study of cities and related disciplines.

show abstract

Section: Identification Data and Methods Of Urban Spatial Structurementioning

confidence: 99%

Research on the relationship between Urban economic development level and urban spatial structure—A case study of two Chinese cities

Zhang

Yuan

2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…POI (point of interest) data express all urban entity abstractions in virtual geographic space and have the advantages of high accuracy, fast data update speed and large data volume etc., so big data have been widely used as new geospatial data in recent years [43], [44]. At present, POI data are mainly used to determine the spatial structure of cities, including the extraction of urban centres [45], the distribution of urban facilities [46], the division of urban functions [47], and the analysis of spatial patterns [48].…”

Section: B Application Of Poi Datamentioning

confidence: 99%

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

2021

View full text Add to dashboard Cite

With the urban built-up area becoming one of the most prominent forms of urban expansion, accurately extracting the urban built-up area is becoming more and more important to judge the urbanization process and evaluate the urban environment. Since it is difficult to significantly improve the accuracy of single satellite data in the extraction of urban built-up areas, this study proposes a method integrating POI (Point of Interest) data and Luojia-1A data to improve the extraction accuracy of urban built-up areas. In this study, integrated Density Graph, OSTU and geometric mean are used to extract urban built-up areas respectively. The highest precision of urban built-up areas extracted before data integration is 74.3% with the highest Kappa value of 0.54; while the highest precision of urban built-up area extracted after data integration is 91.4%, with the highest Kappa value of 0.91. Therefore, it can be concluded that compared with the existing widely used night-light-based methods, this method can integrate the advantages of POI data and Luojia-1A data, that is, it can not only solve the long-term oversaturation phenomenon of night light data, but also can extract urban built-up areas in a more refined way. The method used in this study, which integrates POI data and Luojia-1A data, can not only provide a new method for urban built-up area extraction, but also can play an active guiding role in urban planning and construction.

show abstract

“…The integration of the space-based RS data and the time-based GBD can improve the existing urban land use maps by providing more detailed socioeconomic information and finer spatio-temporal resolution [22][23][24]. Promising progress was made on the applications of integrated RS and GBD on urban land use mapping at different scales and regions [25][26][27][28][29]. For example, Liu et al [30] collected six features (spatial, texture, spatial envelope, rotationinvariant from RS images, Tencent real-time user density, and POI) to identify the urban land use type within each land use parcel.…”

Section: Introductionmentioning

confidence: 99%

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

show abstract

Functional urban land use recognition integrating multi-source geospatial data and cross-correlations

Cited by 84 publications

References 42 publications

Research on the relationship between Urban economic development level and urban spatial structure—A case study of two Chinese cities

Research on the relationship between Urban economic development level and urban spatial structure—A case study of two Chinese cities

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Contact Info

Product

Resources

About