Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification

Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.

Rainfall-runoff risk characteristics of urban function zones in Beijing using the SCS-CN model

Urbanization significantly increases the risk of urban flooding. Therefore, quantitative study of urban rainfall-runoff processes can provide a scientific basis for urban planning and management. In this paper, the built-up region within the Fifth Ring Road of Beijing was selected as the study area. The details of land cover and urban function zones(UFZs) were identified using GIS and RS methods. On this basis, the SCS-CN model was adopted to analyze the rainfall-runoff risk characteristics of the study area. The results showed that:(1) UFZs within different levels of runoff risk varied under different rainfall conditions. The area ratio of the UFZs with high runoff risk increased from 18.90%(for rainfall return period of 1 a) to 54.74%(for period of 100 a). Specifically, urban commercial areas tended to have the highest runoff risk, while urban greening spaces had the lowest.(2) The spatial characteristics of the runoff risks showed an obvious circular distribution. Spatial cluster areas with high runoff risk were mainly concentrated in the center of the study area, while those with low runoff risk were mainly distributed between the fourth and fifth ring roads. The results indicated that the spatial clustering characteristic of urban runoff risk and runoff heterogeneity among different UFZs should be fully considered during urban rainwater management.

Integrating structure and function:mapping the hierarchical spatial heterogeneity of urban landscapes

Background:Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity,which are closely related to myriad urban problems.However,the tools to map and quantify this heterogeneity are lacking.We here developed a new three-level classification scheme,by considering ecosystem types(level 1),urban function zones(level 2),and land cover elements(level 3),to map and quantify the hierarchical spatial heterogeneity of urban landscapes.Methods:We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics.We used a top-down classification procedure by conducting the classification in the order of ecosystem types,function zones,and land cover elements.The classification of the lower level was based on the results of the higher level.We used an objectbased methodology to carry out the three-level classification.Results:We found that the urban ecosystem type accounted for 45.3%of the land within the Shenzhen city administrative boundary.Within the urban ecosystem type,residential and industrial zones were the main zones,accounting for 38.4%and 33.8%,respectively.Tree canopy was the dominant element in Shenzhen city,accounting for 55.6%over all ecosystem types,which includes agricultural and forest.However,in the urban ecosystem type,the proportion of tree canopy was only 22.6%because most trees were distributed in the forest ecosystem type.The proportion of trees was 23.2% in industrial zones,2.2%higher than that in residential zones.That information“hidden”in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management.Conclusions:This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery,infrastructural data available at a municipal level,and object-based spatial analysis.For effective planning and management,the hierarchical levels of landscape classification(level 1),the analysis of use and cover by urban zones(level 2),and the fundamental elements of land cover(level 3),each exposes different respects relevant to city plans and management.

Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify UFZs.However,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution information.In this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings.Subsequently,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance validation.The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline methods.The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.