Functional Urban Regions Research Articles

A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions

Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urban functional regions at the scale of traffic analysis zones (TAZs) by integrating the co-occurrence information and spatial context of POIs. This method has three main steps. First, we utilize buffer zones centered on each POI to construct the urban functional corpus. Second, we use the constructed corpus and GPTEM to train POI type vectors. Third, we cluster the TAZs and annotate the urban functional types in clustered regions by calculating enrichment factors. The results are evaluated by comparing them against manual annotations and food takeout delivery data, showing that the overall identification accuracy of the proposed method (78.44%) is significantly higher than that of a baseline method based on word2vec. Our work can assist urban planners to efficiently evaluate the development of and changes in the functions of various urban regions.

Block2vec: An Approach for Identifying Urban Functional Regions by Integrating Sentence Embedding Model and Points of Interest

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs.

Intraday Variation Mapping of Population Age Structure via Urban-Functional-Region-Based Scaling

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—R2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R2 for each time period was higher than 0.61 and RMSE was less than 0.02.

A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data

Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model.

Comparison of Two Network-Theory-Based Methods for detecting Functional Regions

Abstract Background: Functional regions are abstract, uniformly defined territorial units that form an important basis for many development strategies of a country or a region. Objectives: This study analyses the application of network theory to the detection of such regions. Methods/Approach: Functional regions are analysed using two methods based on the graph theory: the Walktrap algorithm and the chain approach. The quality of the two regionalization methods is analysed using the fuzzy set theory with the revised method. Slovenia was used as a case study. Results: The Walktrap algorithm generated eight functional regions; seven of them corresponded to those identified in previous studies. The only difference occurred in the northwestern mountainous part of Slovenia. The chain approach led to similar results, although it resulted in a huge functional urban region of the capital Ljubljana. Conclusions: The results show that the Walktrap algorithm calculates regions that are more closed, where more workers find work in the home region, than the chain approach.

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments.

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

Re-evaluating polycentric urban structure: A functional linkage perspective

Polycentric urban development has become the buzzword among urban scholars, decision-makers, and planners around the world. From the existing polycentric urban research, a functional approach is increasingly concerned by scholars except for morphological terms. Functional linkages are investigated among the (sub)centers of a polycentric urban system (PUS). However, the (sub)centers are usually pre-defined by a city master plan or identified by a density-sliced approach. However, the definition of the (sub)centers is still dependent on the morphological dimensions rather than functional linkages. To fill the gaps, we proposed a flow-based solution for delineating functional urban regions (FURs). We first built a spatially-embedded network of the entire city within extensive travel flows and then used a community detection method to reveal FURs. The characteristics of the whole PUS and the properties of each FUR are further assessed using complex network analysis. Based on the taxi trajectories of Shanghai, this study shows that the subdivisions of FURs are not necessarily consistent with administrative divisions. The functional linkages are strong among the (sub)centers surrounding the main center, and they are relatively weak among the newly established (sub)centers in the periphery. These findings call for policy interventions to increase the functional linkages of (sub)centers.

Characteristics of and variation in airborne ARGs among urban hospitals and adjacent urban and suburban communities: A metagenomic approach

Environmental antibiotic resistance genes (ARGs) have received much attention, while the characteristics of ARGs carried by particulate matter (PM) as a function of urban functional region are almost unknown. In this study, ARGs carried by PM2.5 and PM10 in an urban hospital, a nearby urban community and the nearest suburban community were detected using metagenomics. In total, 643 ARG subtypes belonging to 22 different ARG types were identified. The chloramphenicol exporter gene, sul1, bacA, and lnuA were the most abundant ARG subtypes in all air samples. The hospital exhibited higher ARG abundance and richness than the nearby communities. ARG profiles depended on functional region: hospital and suburban samples clustered separately, and samples from the nearby urban community interspersed among them. The representation of multidrug and quinolone resistance genes decayed with distance from the hospital to the urban community to the suburban community, indicating that hospital PM may be a hotspot for ARGs encoding proteins conferring multidrug and quinolone resistance. Airborne ARGs carried by PM in the hospital environment were more closely associated with clinically important pathogens than were those in nearby communities. In particular, carbapenemase genes, including blaNDM,blaKPC,blaIMP,blaVIM,and blaOXA-48, were discovered in hospital PM. In the suburban community, crAssphage, a human host-specific bacteriophage, was applied to predict ARG abundance and found to be enriched due to anthropogenic pollution but showed no clear evidence for ARG selection. In the hospital and the nearby urban community, the drivers of ARGs were complex. Our results highlighted that PM ARGs were closely related to human activities and revealed a potential hotspot, which could provide new evidence for further research and consequently mitigate the formation of airborne ARGs and transfer risks.

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data

Overall scientific planning of urbanization layout is an important component of the new period of land spatial planning policies. Defining the main functions of different spaces and dividing urban functional areas are of great significance for optimizing the land development pattern. This article identifies and analyses urban functional areas from the perspective of data mining. The results of this method are consistent with the actual situation. In this paper, representative taxi trajectory data are selected as the research basis of urban functional areas. First, based on trajectory data from Didi Chuxing within the high-speed road surrounding Chengdu, we generated trajectory time sequence data and used the dynamic time warping (DTW) algorithm to generate a time series similarity matrix. Second, we utilized the K-medoid clustering algorithm to generate preliminary results of land clustering and selected the results with high classification accuracy as the training samples. Then, the k-nearest neighbour (KNN) classification algorithm based on DTW was performed to classify and identify the urban functional areas. Finally, with the help of point-of-interest (POI) auxiliary analysis, the final functional layout in Chengdu was obtained. The results show that the spatial structure of Chengdu is complex and that the urban functions are interlaced, but there are still rules that are followed. Moreover, traffic volume and inflow data can better reflect the travel rules of residents than simple taxi on–off data. The original DTW calculation method has high temporal complexity, which can be improved by normalization and the reduction of time series dimensionality. The semi-supervised learning classification method is also applicable to trajectory data, and it is best to select training samples from unsupervised learning. This method can provide a theoretical basis for urban land planning and has auxiliary and guiding value for urbanization layout in the context of land spatial planning policies in the new era.

Combining travel and population data through a bivariate spatial analysis to define Functional Urban Regions

The aim of this paper is to provide support to the issue of defining Functional Urban Regions (FURs). We carried out an exploratory study in which both commuting trips and population data were used in a combined setting. To do this, Bivariate Exploratory Spatial Data Analysis was used, enabling permuted analyses of the variables considered. The municipalities of São Paulo state, Brazil, were used in the case study investigated in this research. We observed that this combined analysis has more satisfactory results when compared to the analyses of variables in an isolated way. There is also a greater correlation of the results obtained from bivariate analysis with the official delimitations. Considering the possible permuted analyses, which are travel data versus population or population versus travel data, the first showed more reliable results in terms of identifying municipalities with strong correlations between the two variables. This can be observed both in areas within official FURs and throughout the territory, highlighting the areas of regional influence.

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data

Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed.

RECOGNITION OF URBAN FUNCTIONAL REGIONS AT STREET SCALE BASED ON LJ1-01 NIGHT-TIME LIGHT REMOTE SENSING AND MOBILE BIG DATA

Abstract. Night-time light (NTL) remote sensing data has been widely used in the analysis of human activities at night for a large extent, such as light pollution monitoring and recognition of urban functional regions. In most previous studies, the spatial resolutions of NTL remote sensing data are rather low (e.g., 500 m or coarser). Besides, remote sensing classification of land use rather than land cover types is often a hard task. Due to the reasons, it is difficult to meet the demand of urban refined management. In this study, mobile big data and Luojia1-01 (LJ1-01) NTL remote sensing satellite data with higher spatial resolution are adopted for extracting urban functional regions at the street-level scale. Taking Shenzhen city as a case, mobile big data (i.e., the volume of mobile devices) with the accuracy of approximate 150 m (i.e., 7-bit GeoHash encoding format) is integrated with NTL remote sensing data. After a hot spot analysis, the correlation between the two types of data are analysed. The typical supervised classification algorithms including KNN, SVM and random forest are used to distinguish urban functional regions, which are represented by five types, namely residential, business and commercial, industrial, transportation and other areas. The results show the effectiveness in extracting land use types in cities by combination of Luojia1-01 night-time light remote sensing and mobile big data. This study provides a basis for monitoring night light pollution of residential area, urban planning and the construction of smart cities.

Delineating urban functional regions by considering interaction cohesiveness and function diversity

Abstract. A functional region is usually defined as a region characterized by not only interaction cohesiveness but also function diversity. The former indicates that places within a region should share a cohesive interacting pattern of human movements, such as commute or daily travel; the latter means that the places should possess diverse functions like residence, commerce, etc. Due to these two characteristics, people living and moving in one region can acquire almost all their daily needs, and they do not need to frequently move to other regions. In other words, a functional region resembles a life circle. Based on this definition, many previous studies have developed different methods to analyse human flow data so that they can properly profile different types of functional regions like Local labour market area (LLMA) or daily urban system (DUS), which helps people better understand how functional regions compose an area. Existing methods have considered interaction cohesiveness in a functional region, but function diversity is ignored. This is because these studies only used specific-purpose-trip data, such as journey to work or to shop. These kinds of trips describe people moving from their home to their office or to shopping malls, so each trip has an inherent distinction of functions between its origin and destination; thus, a functional region consisting of these trips indirectly owns function diversity. However, this framework has some problems. First, it cannot profile a functional region shaped by composite functions, for only one trip purpose can be taken into account each time. Second, collecting the data of specific-purpose trips needs either some questionnaires or some surveys, so it is often costly and time consuming. Finally, the purpose-unrecorded-trip data provided by rapidly developed smart card systems in several countries can be collected quickly, yet it cannot properly profile functional regions through the aforementioned framework due to the lack of trip purposes. Therefore, we propose a new spatial partitioning algorithm which can simultaneously consider both interaction cohesiveness and function diversity in profiling functional regions. Compared with previous methods, the proposed algorithm can better profile functional regions from many purpose-unrecorded-trip data based on the inclusion of land-use data. Our results provide deeper insights for understanding how various functions of lands and human movements together shape the boundary of a region. It could be significant reference for developing policies for urban planning or public transportation management.

Identification of urban regions' functions in Chengdu, China, based on vehicle trajectory data.

Data about human trajectories has been widely used to study urban regions that are attractive to researchers and are considered to be hotspots. It is difficult, however, to quantify the function of urban regions based on the varieties of human behavior. In this research, we developed a clustering method to help discover the specific functions that exist within urban regions. This method applies the Gaussian Mixture Model (GMM) to classify regions’ inflow and trip count characteristics. It regroups these urban regions using the Pearson Correlation Coefficient (PCC) clustering method based on those typical characteristics. Using a large amount of vehicle trajectory data (approximately 1,500,000 data points) in the Chinese city of Chengdu, we demonstrate that the method can discriminate between urban functional regions, by comparing the proportion of surface objects within each region. This research shows that vehicle trajectory data in different functional urban regions possesses different time-series curves, while similar types of functional regions can be identified by these curves. Compared with remote sensing images and other statistical methods which can provide only static results, our research can provide a timely and effective approach to determine an urban region’s functions.

Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach

Understanding the complex heterogeneity of traffic flow for the road network is of great importance to relieving traffic congestion and designing traffic control strategies. This study proposes a 3-stage pattern extraction approach to discover the heterogeneous spatiotemporal traffic flow patterns from the enormous observations: The non-negative tensor decomposition technique is utilized to extract the features of traffic flow in the within-one-day time, day-to-day and spatial segment modes; The spectral clustering is performed in the low-dimensional feature space, and the Gaussian similarity matrix is modified by the spatial proximity coefficient to integrate the spatial compactness into the clustering algorithm; The large-scale road network is partitioned into the independent and compact subnetworks with similar traffic congestion levels, and the macroscopic traffic flow relationships are fitted for each pattern. Research results indicate that the statistical distribution of traffic flow for each pattern is diverse; the heterogeneity of traffic patterns is verified by the low-scatter diagrams of macroscopic traffic flow relation for each subnetwork; the congested subnetworks are detected for both the peak hours and non-peak hours; the structures of subnetworks are highly concordant with the urban functional regions, varying with the different periods.

The Geography of Borrowing Size: Exploring Spatial Distributions for German Urban Regions

AbstractThis paper contributes to the discussion on borrowing size effects. According to this concept, smaller cities that are part of larger functional urban regions can utilise metropolitan functions and economic externalities, thereby boosting their regional performance. This is conceptualised in the four‐dimension scheme of Meijers and Burger. Consequently, this paper analytically explores and operationalises the borrowed size concept and reveals insights into the relation and spatial distribution of four types of effects: borrowed size, borrowed performance, borrowed function and agglomeration shadow. Research on these spatial effects is applied on a small‐scale spatial level and integrates all municipalities in German urban regions. We find different geographies of effects among the four‐dimensions of borrowed size. City size and centrality degree show a significant influence on positioning in the four borrowed size effect types.

Selective migration of population in functional urban regions of Slovakia

ABSTRACTMajority of research on urban development perceive migration as internally homogeneous phenomenon that influences growth and spatial redistribution of population. This enables to identify certain general migration patterns, but understanding regularities and consequences requires looking at internal structure of migration. This paper aims to visualise the impact of selective migration to the population structures across multiple spatial scales and in a complex way taking Slovakia as the case study. By this means spatial patterns of migration are demonstrated in a more efficient way. The resulting maps show the impact of migration is selective at various spatial levels regarding age and educational attainment. In comparison with general migration, selective migration shows stronger spatial polarisation in Slovakia.