Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

► Journal Browser

Smart City Development and Remote Sensing Application in Urban Ecology

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 35625

Share This Special Issue

Special Issue Editors

Dr. Peter van Bodegom

E-Mail Website
Guest Editor

Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands
Interests: urban ecosystem services; urban biodiversity; data science for optimal planning; assimilation of remote sensing data; functional diversity

Prof. Dr. Tao Lin

E-Mail Website
Guest Editor

Institute of urban environment, Chinese academy of sciences, Xiamen 361024, China
Interests: urban environmental management and planning, urban ecology, urban metabolism
Special Issues, Collections and Topics in MDPI journals

Dr. Pfaender Fabien

E-Mail Website
Guest Editor

UTSEUS, Shanghai University, Costech EA2223, Université de Technologie de Compiègne, Sorbonne Université, Paris, France
Interests: urban data science; urban networks; natural language processing for spatial features; urban complex systems

Special Issue Information

Dear Colleagues,

Cities became the main habitat for humanity in the early 21st century, and urbanization keeps increasing with no sign of a slowdown. In this context, urban ecology plays an important role in achieving a sustainable development at both the global and regional scale. Urban ecology considers cities as a human/environment coupled system in order to study interactions between human socioeconomic systems and the natural ecosystems in or surrounding the cities. Moreover, it provides crucial understanding in the mechanism of the natural ecosystems sustaining the city development. Recently, computer science and internet communication technology (ICT) and modern remote observing systems, urban data science, remote sensing, and big data application have begun to play an increasingly important role in urban sustainability. They help to quantitatively understand the urban form, its functions, and human behaviors in cities. The integration of urban ecology with new developments in data science will be instrumental in harvesting data, improving models, and proposing new methods. Data-science-related methods and remote sensing applications enrich the theories and methodologies of urban ecosystem sciences and aid towards a better understanding and predicting spatiotemporal patterns, trade-offs, and synergies, which are crucial elements in our attempts to improve urban planning.

This open-access Special Issue invites high-quality and innovative scientific articles, which include innovative and multidisciplinary researchers on the latest developments in urban data science, smart city application, remote sensing methods, and pilot urban ecosystem studies in the world, exploring the potential cooperation between the ecological city and the smart city. Potential topics include but are not limited to the following:

Integration of the smart city and big data in urban ecology;
Frontier applications of remote sensing in the smart city and the ecological city;
Big data applications in urban ecology;
Spatiotemporal analysis of the impact of urbanization on natural ecosystems;
Sustainable city/community development based on big data;
Spatiotemporal monitoring and modeling of urban ecology based on remote sensing;
Smart urban system management and risk;
Spatiotemporal trade-offs and synergies in urban ecosystem services.

Dr. Peter van Bodegom
Dr. Tao Lin
Dr. Pfaender Fabien
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

Download All Papers

Research

Jump to: Other

17 pages, 5309 KiB

Open AccessArticle

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

by Zhuoqun Zhao, Xuchao Yang, Han Yan, Yiyi Huang, Guoqin Zhang, Tao Lin and Hong Ye

Remote Sens. 2021, 13(21), 4346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214346 - 28 Oct 2021

Cited by 15 | Viewed by 3527

Abstract

The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km² spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km² BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO₂ emission peak and carbon neutralization. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

17 pages, 4647 KiB

Open AccessArticle

Time-Series Landsat Data for 3D Reconstruction of Urban History

by Wenjuan Yu, Chuanbao Jing, Weiqi Zhou, Weimin Wang and Zhong Zheng

Remote Sens. 2021, 13(21), 4339; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214339 - 28 Oct 2021

Cited by 3 | Viewed by 1995

Abstract

Accurate quantification of vertical structure (or 3D structure) and its change of a city is essential for understanding the evolution of urban form, and its social and ecological consequences. Previous studies have largely focused on the horizontal structure (or 2D structure), but few on 3D structure, especially for long time changes, due to the absence of such historical data. Here, we present a new approach for 3D reconstruction of urban history, which was applied to characterize the urban 3D structure and its change from 1986 to 2017 in Shenzhen, a megacity in southern China. This approach integrates the contemporary building height obtained from the increasingly available data of building footprint with building age estimated based on the long-term observations from time-series Landsat imagery. We found: (1) the overall accuracy for building change detection was 87.80%, and for the year of change was 77.40%, suggesting that the integrated approach provided an effective method to cooperate horizontal (i.e., building footprint), vertical (i.e., building height), and temporal information (i.e., building age) to generate the historical data for urban 3D reconstruction. (2) The number of buildings increased dramatically from 1986 to 2017, by eight times, with an increased proportion of high-rise buildings. (3) The old urban areas continued to have the highest density of buildings, with increased average height of buildings, but there were two emerging new centers clustered with high-rise buildings. The long-term urban 3D maps allowed characterizing the spatiotemporal patterns of the vertical dimension at the city level, which can enhance our understanding on urban morphology. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Figure 1

16 pages, 8647 KiB

Open AccessArticle

Quantitatively Assessing Ecological Stress of Urbanization on Natural Ecosystems by Using a Landscape-Adjacency Index

by Meixia Lin, Tao Lin, Laurence Jones, Xiaofang Liu, Li Xing, Jinling Sui, Junmao Zhang, Hong Ye, Yuqin Liu, Guoqin Zhang and Xin Lu

Remote Sens. 2021, 13(7), 1352; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071352 - 01 Apr 2021

Cited by 9 | Viewed by 2378

Abstract

Urban spatial expansion poses a threat to regional ecosystems and biodiversity directly through altering the size, shape, and interconnectivity of natural landscapes. Monitoring urban spatial expansion using traditional area-based metrics from remote sensing provides a feasible way to quantify this regional ecological stress. However, variation in landscape-adjacency relationships (i.e., the adjacency between individual landscape classes) caused by urban expansion is often overlooked. In this study, a novel edge-based index (landscape-adjacency index, LAdI) was proposed based on the spatial-adjacency relationship between landscape patches to measure the regional ecological stress of urban expansion on natural landscapes. Taking the entire Yangtze River Delta Urban Agglomerations (YRD) as a study area, we applied the LAdI for individual landscape classes (V_i) and landscape level (LV) to quantitatively assess change over time in the ecological stress of YRD from 1990 to 2015 at two spatial scales: municipal scale and 5 km-grid scale. The results showed that the vulnerable zones (LV ≥ 0.6) were mainly distributed in the north of the YRD, and cultivated land was the most vulnerable natural landscape (V_i ≥ 0.6) at the 5 km-grid scale. The most vulnerable landscape at the municipal scale was cultivated land in 19 of 26 cities in each period, and that in the remaining 7 cities varied at distinct urbanization stages. We used scatter diagrams and Pearson correlation analysis to compare the edge-based LAdI with an area-based index (percent of built-up area, PB) and found that: LV and PB had a significant positive correlation at both the municipal scale and 5 km-grid scale. But there were multiple LVs with different values corresponding to one PB with the same value at the 5 km-grid scale. Both indexes could represent the degree of urban expansion; however, the edge-based metric better quantified ecological stress under different urban-sprawl patterns sharing the same percent of built-up area. As changes in land use affect both the size and edge effect among landscape patches, the area-based PB and the edge-based LAdI should be applied together when assessing the ecological stress caused by urbanization. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Figure 1

19 pages, 3164 KiB

Open AccessArticle

Reverse Thinking: A New Method from the Graph Perspective for Evaluating and Mitigating Regional Surface Heat Islands

by Zhaowu Yu, Jinguang Zhang, Gaoyuan Yang and Juliana Schlaberg

Remote Sens. 2021, 13(6), 1127; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061127 - 16 Mar 2021

Cited by 29 | Viewed by 3498

Abstract

Accurately locating key nodes and corridors of an urban heat island (UHI) is the basis for effectively mitigating a regional surface UHI. However, we still lack appropriate methods to describe it, especially considering the interaction between UHIs and the role of connectivity (network). Specifically, previous studies paid much attention to the raster and vector perspective—based on standard landscape configuration metrics that only provide an overall statistic over the entire study area without further indicating locations where different types of pattern and fragmentation occur. Therefore, by reverse thinking, here we attempt to propose a new method from the graph perspective which integrates morphological spatial pattern analysis (MSPA)—which is used to characterize binary patterns with emphasis on connections between their parts as measured at varying analysis scales, and habitat availability indices to evaluate and mitigate regional surface UHI. We selected the Pearl River Delta Metropolitan Region (PRDR), one of the most rapidly urbanized regions in the world as the case study (1995–2015). The results of the case study showed: (1) the core (UHI) type accounts for the vast majority of the MSPA model, with the relative land surface temperature (LST) rises, the proportion of the core type will increase, and it could influence the edge (UHI) type significantly; (2) the branch, bridge, and islet (UHI) types have similar results to the lower temperature (4 < Relative LST ≤ 6) area and account for the majority, indicating that these types are more susceptible to their surrounding environment; (3) the importance and extreme importance area (node) from 1995 to 2015 have increased significantly and mainly distributed in the urbanized areas, which means cooling measures need to be implemented in these areas in order of priority. Shifting the research logic of UHI evaluation and mitigation from “patch” to “network”, we hold the point that the method (reverse thinking) has significant theoretical and practical implications for mitigating regional UHI and urban climate-resilience. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Figure 1

23 pages, 5549 KiB

Open AccessArticle

The Challenge of the Urban Compact Form: Three-Dimensional Index Construction and Urban Land Surface Temperature Impacts

by Han Yan, Kai Wang, Tao Lin, Guoqin Zhang, Caige Sun, Xinyue Hu and Hong Ye

Remote Sens. 2021, 13(6), 1067; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061067 - 11 Mar 2021

Cited by 20 | Viewed by 3012

Abstract

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

19 pages, 8392 KiB

Open AccessArticle

Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature

by Dakang Wang, Tao Yu, Yan Liu, Xingfa Gu, Xiaofei Mi, Shuaiyi Shi, Meihong Ma, Xinran Chen, Yin Zhang, Qixin Liu, Faisal Mumtaz and Yulin Zhan

Remote Sens. 2021, 13(2), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020225 - 11 Jan 2021

Cited by 9 | Viewed by 2667

Abstract

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRF_HR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRF_HR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRF_HR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R² amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRF_HR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R², as well as the small RMSE and BIAS amounts, indicate that ET_WRF_HR has achieved a very good performance. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Figure 1

29 pages, 19051 KiB

Open AccessArticle

Monitoring Urban Green Infrastructure Changes and Impact on Habitat Connectivity Using High-Resolution Satellite Data

by Dorothy Furberg, Yifang Ban and Ulla Mörtberg

Remote Sens. 2020, 12(18), 3072; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183072 - 19 Sep 2020

Cited by 22 | Viewed by 5990

Abstract

In recent decades, the City of Stockholm, Sweden, has grown substantially and is now the largest city in Scandinavia. Recent urban growth is placing pressure on green areas within and around the city. In order to protect biodiversity and ecosystem services, green infrastructure is part of Stockholm municipal planning. This research quantifies land-cover change in the City of Stockholm between 2003 and 2018 and examines what impact urban growth has had on its green infrastructure. Two 2018 WorldView-2 images and three 2003 QuickBird-2 images were used to produce classifications of 11 land-cover types using object-based image analysis and a support vector machine algorithm with spectral, geometric and texture features. The classification accuracies reached over 90% and the results were used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2003 and 2018, including the generation of land-cover change statistics in relation to administrative boundaries and green infrastructure. For components of the green infrastructure, i.e., habitat networks for selected sensitive species, habitat network analysis for the European crested tit (Lophophanes cristatus) and common toad (Bufo bufo) was performed. Between 2003 and 2018, urban areas increased by approximately 4% while green areas decreased by 2% in comparison with their 2003 areal amounts. The most significant urban growth occurred through expansion of the transport network, paved surfaces and construction areas which increased by 12%, mainly at the expense of grassland and coniferous forest. Examination of urban growth within the green infrastructure indicated that most land area was lost in dispersal zones (28 ha) while the highest percent change was within habitat for species of conservation concern (14%). The habitat network analysis revealed that overall connectivity decreased slightly through patch fragmentation and areal loss mainly caused by road expansion on the outskirts of the city. The habitat network analysis also revealed which habitat areas are well-connected and which are most vulnerable. These results can assist policymakers and planners in their efforts to ensure sustainable urban development including sustaining biodiversity in the City of Stockholm. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

16 pages, 3096 KiB

Open AccessArticle

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM_2.5 Levels

by Zhao-Yue Chen, Jie-Qi Jin, Rong Zhang, Tian-Hao Zhang, Jin-Jian Chen, Jun Yang, Chun-Quan Ou and Yuming Guo

Remote Sens. 2020, 12(18), 3008; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183008 - 15 Sep 2020

Cited by 19 | Viewed by 3444

Abstract

The immense problem of missing satellite aerosol retrievals (Aerosol Optical Depth, (AOD)) detrimentally affects the prediction ability of ground-level PM_2.5 concentrations and may lead to unavoidable biases. An appropriate missing-imputation method has not been well developed to date. This study developed a two-stage approach (AOD-imputation stage and PM_2.5-prediction stage) to predict short-term PM_2.5 exposure in mainland China from 2013–2018. At the AOD-imputation stage, geostatistical methods and machine learning (ML) algorithms were examined to interpolate 1 km satellite aerosol retrievals. At the PM_2.5-prediction stage, the daily levels of PM_2.5 were predicted at a resolution of 1 km, based on interpolated AOD and meteorological data. The statistical performances of the different interpolation methods were comprehensively compared at each stage. The original coverage of retrieved AOD was 15.46% on average. For the AOD-imputation stage, ML methods produced a higher coverage (98.64%) of AOD than geostatistical methods (21.43–87.31%). Among ML algorithms, random forest (RF) or extreme gradient boosted (XG-interpolated) AOD produced better interpolated quality (CV R² = 0.89 and 0.85) than other algorithms (0.49–0.78), but XGBoost required only 15% of the computing time of RF. For the PM_2.5 predicted stage, neither RF-AOD nor XG-AOD could guarantee higher accuracy in PM_2.5 estimations (CV R² = 0.88 (RF or XG-AOD) compared to 0.85 (original)), or more stable spatial and temporal extrapolation (spatial, (temporal) CV R² = 0.83 (0.83), 0.82 (0.82), and 0.65 (0.61) for RF, XG, and original). For the AOD-imputation stage, the missing-filled efficiency depended more on external information, while the missing-filled accuracy relied more on model structure. For the PM_2.5 predicted stage, efficient AOD interpolation (or the ability to eliminate the missing data) was a precondition for the stable spatial and temporal extrapolation, while the quality of interpolated AOD showed less significant improvements. It was found that XG-AOD is a better choice to estimate daily PM_2.5 exposure in health assessments. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

22 pages, 7181 KiB

Open AccessArticle

Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

by Qiwei Ma, Zhaoya Gong, Jing Kang, Ran Tao and Anrong Dang

Remote Sens. 2020, 12(16), 2513; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162513 - 05 Aug 2020

Cited by 16 | Viewed by 3853

Abstract

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 4355 KiB

Open AccessTechnical Note

Measuring the Service Capacity of Public Facilities Based on a Dynamic Voronoi Diagram

by Haifu Cui, Liang Wu, Sheng Hu and Rujuan Lu

Remote Sens. 2021, 13(5), 1027; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13051027 - 09 Mar 2021

Cited by 5 | Viewed by 2661

Abstract

The supply–demand relationship of urban public service facilities is the key to measuring a city’s service level and quality, and a balanced supply–demand relationship is an important indicator that reflects the optimal allocation of resources. To address the problem presented by the unbalanced distribution of educational resources, this paper proposes a dynamic Voronoi diagram algorithm with conditional constraints (CCDV). The CCDV method uses the Voronoi diagram to divide the plane so that the distance from any position in each polygon to the point is shorter than the distance from the polygon to the other points. In addition, it can overcome the disadvantage presented by the Voronoi diagram’s inability to use the nonspatial attributes of the point set to precisely constrain the boundary range; the CCDV method can dynamically plan and allocate according to the school’s capacity and the number of students in the coverage area to maintain a balance between supply and demand and achieve the optimal distribution effect. By taking the division of school districts in the Bao’an District, Shenzhen, as an example, the method is used to obtain a school district that matches the capacity of each school, and the relative error between supply and demand fluctuates only from −0.1~0.15. According to the spatial distribution relationship between schools and residential areas in the division results, the schools in the Bao’an District currently have an unbalanced distribution in some areas. A comparison with the existing school district division results shows that the school district division method proposed in this paper has advantages. Through a comprehensive analysis of the accessibility of public facilities and of the balance of supply and demand, it is shown that school districts based on the CCDV method can provide a reference for the optimal layout of schools and school districts. Full article

(This article belongs to the Special Issue Smart City Development and Remote Sensing Application in Urban Ecology)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Smart City Development and Remote Sensing Application in Urban Ecology

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (10 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI