Urban Computing—Data, Techniques, Tools, and Applications

Topic Information

Dear Colleagues,

Since the term “urban computing” was first introduced, the field has continued to evolve and grow. In particular, new sources of data and advances in technologies to process this data mean that the role of urban computing in shaping our cities has intensified. Urban computing is a combination of computer science with other urban fields such as urban planning, design, sociology, transportation, environmental science, history, geography, and cultural studies. Urban computing strives to generate data-driven frameworks to help us analyse and understand urban processes and to solve urban challenges.

The availability of urban data has increased dramatically in the last decade, which has led to greater impact for urban computing. The descriptive power of the data is potentially magnified, albeit with additional challenges, when big data sources are included in urban computing frameworks. These data are collected using various methods by different devices, formats, standards, and tools which need new data infrastructures and methods to address urban challenges. In parallel with the increased supply of data and ever-increasing technological infrastructure, new tools and techniques have emerged to support urban computing. It is now common to find changes in computational tools used within urban computation tools, including (open-source) software, heterogeneous architectures and resources, data storage (cloud), and computational methods. The power of urban computing is seen in the variety of applications which are used by different actors, including governmental, public, and private sectors as well as researchers and citizens.

The focus of this Topical Collection is on data, techniques, tools, and applications of urban computing to identify, analyse, understand, highlight, and simplify urban complexities, solve wicked urban problems, promote participatory planning, and improve the quality of life in cities through digital technologies. Topics of interest include, but are not limited to, the following: 

1. Urban big data and text analytics;
2. Smart mobility and smart environments;
3. Spatial decision support systems and urban dashboards;
4. Acquisition and processing of high resolution remotely sensed data (Lidar, image, etc.) for urban applications;
5. Location-based service in smart cities;
6. (Location) privacy and blockchain technology in smart city;
7. Internet of things and sensor networks for smart cities and mobility;
8. Computational social science;
9. Data acquisition, storage, management, analysis, sharing;
10. Geovisualisation and geosimulation of urban dynamics;
11. 3D geovisualisation;
12. Agent-based simulation for urban dynamics;
13. Urban sensor network data and applications;
14. Augmented reality (AR), extended reality (ER), and virtual reality (VR) for urban applications.

Dr. Gavin McArdle
Prof. Dr. Mir Abolfazl Mostafavi
Dr. Hamidreza Rabiei-Dastjerdi
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800
Land land	3.9	3.7	2012	14.8 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Smart Cities smartcities	6.4	8.5	2018	20.2 Days	CHF 2000
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

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Published Papers (15 papers)

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51 pages, 6347 KiB  

Open AccessArticle

Smart and Sentient Retail High Streets

by Paul M. Torrens

Smart Cities 2022, 5(4), 1670-1720; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities5040085 - 29 Nov 2022

Cited by 4 | Viewed by 4242

Abstract

Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart [...] Read more.

Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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21 pages, 20964 KiB  

Open AccessArticle

Environmental Conditions in Middle Eastern Megacities: A Comparative Spatiotemporal Analysis Using Remote Sensing Time Series

by Shahin Mohammadi, Mohsen Saber, Saeid Amini, Mir Abolfazl Mostafavi, Gavin McArdle and Hamidreza Rabiei-Dastjerdi

Remote Sens. 2022, 14(22), 5834; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225834 - 17 Nov 2022

Cited by 4 | Viewed by 2165

Abstract

Rapid and timely evaluation and monitoring of the urban environment has gained significant importance in understanding the state of urban sustainability in metropolises. Multi-source remote sensing (RS) data are a valuable source for a comprehensive understanding of urban environmental changes in developing countries. [...] Read more.

Rapid and timely evaluation and monitoring of the urban environment has gained significant importance in understanding the state of urban sustainability in metropolises. Multi-source remote sensing (RS) data are a valuable source for a comprehensive understanding of urban environmental changes in developing countries. However, in the Middle East, a region with several developing countries, limited study has been conducted to understand urban environmental changes. In this study, to evaluate the changes in the urban environment, 32 metropolises in the Middle East were studied between 2000 and 2019. For this purpose, a comprehensive environmental index (CEI) integrated with Google Earth Engine (GEE) platform for processing and analysis is introduced. The results show degraded environmental conditions in 19 metropolises based on a significant increasing trend in the time series of the CEI index. The highest increasing trend in the value of the CEI was observed in the cities of Makkah, Jeddah, Basra, Riyadh, and Sana’a. The results also show that the percentage of urban areas in all 32 cities that falls into the degraded class varies from 5% to 75% between 2005 and 2018. The results of CEI changes in megacities, such as Ajman, Tehran, Jeddah, Makkah, Riyadh, Karaj, and Sana’a show that these cities have increasingly suffered from the degradation of environmental conditions since 2001. According to the results, it is recommended to pay more attention to environmental issues regarding the future of urban development in these cities. The proposed approach in this study can be implemented for environmental assessment in other regions. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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17 pages, 6910 KiB  

Open AccessArticle

An Automatic Approach to Extracting Large-Scale Three-Dimensional Road Networks Using Open-Source Data

by Yang Chen, Xin Yang, Ling Yang and Jiayu Feng

Remote Sens. 2022, 14(22), 5746; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225746 - 14 Nov 2022

Cited by 4 | Viewed by 1874

Abstract

3D road networks are amongst the indispensable elements of a smart city, which has been explored in various ways. However, researchers still faces challenges extracting 3D networks on a large scale. The global digital surface models (DSMs) with relatively high spatial resolution make [...] Read more.

3D road networks are amongst the indispensable elements of a smart city, which has been explored in various ways. However, researchers still faces challenges extracting 3D networks on a large scale. The global digital surface models (DSMs) with relatively high spatial resolution make it possible to extract 3D road networks. Nevertheless, the complete and accurate elevation of road networks cannot be obtained directly because of the limitation in sensors on the DSM production platform. Thus, we proposed a novel approach to extract large-scale 3D road networks, integrating terrain correction and road engineering rule constraint, by using the Advanced Land Observing Satellite World 3D-30 m DSM, OpenStreetMap and FABDEM. The simplification and terrain correction algorithm were applied to remove most of the edges with excessive grades and reduced the negative impact of the built-up environment in DSM on the extraction accuracy. Moreover, the tunnel parts of the 3D road networks were refined based on road engineering standards. Nanjing of China, Aalborg of Denmark and Los Angeles of the United States are selected as study areas. Using 3D road networks from unmanned aerial vehicle photogrammetry, light detection and ranging and Google Earth as references, we validated the road elevation accuracy of our method and obtained an overall root-mean-square error of 3.80 m and a mean absolute error of 1.94 m. The 3D topology of interchanges with different radii was reconstructed completely. Overall, our work is an endeavour to utilise multiple open-source data to extract large-scale 3D road networks and benefits future research related to smart city reconstruction and 3D urban analysis. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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18 pages, 8576 KiB  

Open AccessArticle

A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods

by Ali Khosravi Kazazi, Fariba Amiri, Yaser Rahmani, Raheleh Samouei and Hamidreza Rabiei-Dastjerdi

Sustainability 2022, 14(21), 14106; https://0-doi-org.brum.beds.ac.uk/10.3390/su142114106 - 28 Oct 2022

Cited by 6 | Viewed by 2090

Abstract

The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to [...] Read more.

The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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19 pages, 5900 KiB  

Open AccessArticle

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

by Jun Zhang, Xue Zhang, Xueping Tan and Xiaodie Yuan

Land 2022, 11(8), 1212; https://0-doi-org.brum.beds.ac.uk/10.3390/land11081212 - 01 Aug 2022

Cited by 14 | Viewed by 2361

Abstract

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important [...] Read more.

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km², accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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25 pages, 3954 KiB  

Open AccessArticle

Spatial Evaluation of Villages and Towns Based on Multi-Source Data and Digital Technology: A Case Study of Suining County of Northern Jiangsu

by Yuan Zhang, Xiang Ji, Liang Sun and Yaxi Gong

Sustainability 2022, 14(13), 7603; https://0-doi-org.brum.beds.ac.uk/10.3390/su14137603 - 22 Jun 2022

Cited by 7 | Viewed by 1717

Abstract

Based on the research on the current situation and transformation path of the spatial construction of villages and towns in northern Jiangsu, “compactness” and “convenience” are extracted as the elements of spatial evaluation. With multi-source data, comprehensively using ENVI remote sensing image interpretation, [...] Read more.

Based on the research on the current situation and transformation path of the spatial construction of villages and towns in northern Jiangsu, “compactness” and “convenience” are extracted as the elements of spatial evaluation. With multi-source data, comprehensively using ENVI remote sensing image interpretation, GIS spatial analysis, Fragstats landscape index calculation, entropy weight–TOPSIS comprehensive evaluation, and SPSS cluster analysis, a “digital full cycle” of a research framework for the spatial evaluation of villages and towns is built. In this paper, Suining County is taken as the research object, and the spatial construction level of its villages and towns is studied. The research results show that at the county level, the spatial compactness of villages and towns roughly presents the characteristics of an “X” pattern, decreasing from the middle to the four sides, while facility convenience generally presents the characteristics of a right “人” (Chinese character) pattern. At the town level, facility convenience basically presents the pattern characteristics of the “center-node” differentiation structure. The research aims to guide villages and towns to solve the current dilemma of spatial construction, promote the construction of digital villages and towns, and impel the digital transformation of the village and town evaluation system, data, and methods, so as to provide real-time, quantitative, and accurate data and method support for planning and decision-making in villages and towns. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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14 pages, 3935 KiB  

Open AccessArticle

Quantifying Dynamic Coupling Coordination Degree of Human–Environmental Interactions during Urban–Rural Land Transitions of China

by Bowen Cai, Zhenfeng Shao, Shenghui Fang and Xiao Huang

Land 2022, 11(6), 935; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060935 - 17 Jun 2022

Cited by 7 | Viewed by 1986

Abstract

Urban–rural land transition and the coordination of coupled human–environmental systems are two important issues in the process of global urban–rural development. Although existing studies have explored the coupling coordination degree (CCD) of human–environmental interactions under the context of urbanization, few studies have taken [...] Read more.

Urban–rural land transition and the coordination of coupled human–environmental systems are two important issues in the process of global urban–rural development. Although existing studies have explored the coupling coordination degree (CCD) of human–environmental interactions under the context of urbanization, few studies have taken land transitions into consideration. In this study, we investigated the dynamics of CCD in China from 2001 to 2018 using multisource remote sensing data and quantified the CCD changes in land transitions among urban construction land (UCL), rural residential land (RRL), and non-construction land (NCL). The CCD alterations mainly occurred in the decline in NCL stock, the increase in UCL stock, and especially the losses during RRL to NCL transfers. We urge academics and government decision-makers to pay more attention to the CCD transfers and losses during urban–rural transitions. This study provides scientific guidance for the development of urban–rural integration and is expected to assist the coordinated evaluation of human–environmental interactions in the process of sustainable development. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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17 pages, 6950 KiB  

Open AccessArticle

Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle

by Longjun Qin, Peng Mao, Zhenbang Xu, Yang He, Chunhua Yan, Muhammad Hayat and Guo-Yu Qiu

Remote Sens. 2022, 14(9), 2093; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092093 - 27 Apr 2022

Cited by 2 | Viewed by 2080

Abstract

With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment [...] Read more.

With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment and monitoring of urban vegetation damage after typhoons is important, as they contribute to post-disaster recovery and resilience efforts. Hence, this study examined the application of the easy-to-use and cost-effective Unmanned Aerial Vehicle (UAV) oblique photography technology and proposed an improved detection and diagnostic measure for the assessment of street-level damage to urban vegetation caused by the super typhoon Mangkhut in Shenzhen, China. The results showed that: (1) roadside trees and artificially landscaped forests were severely damaged; however, the naturally occurring urban forest was less affected by the typhoon. (2) The vegetation height of roadside trees decreased by 20–30 m in most areas, and that of artificially landscaped forests decreased by 5–15 m; however, vegetation height in natural forest areas did not change significantly. (3) The real damage to vegetation caused by the typhoon is better reflected by measuring the change in vegetation height. Our study validates the use of UAV remote sensing to accurately measure and assess the damage caused by typhoons to roadside trees and urban forests. These findings will help city planners to design more robust urban landscapes that have greater disaster coping capabilities. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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12 pages, 3399 KiB  

Open AccessArticle

Spatial Features of Urban Expansion in Vietnam Based on Long-Term Nighttime Lights Data

by Keyang Zhou, Yutian Liang, Chen Zhong, Jiaqi Zeng and Zhengke Zhou

Land 2022, 11(5), 601; https://0-doi-org.brum.beds.ac.uk/10.3390/land11050601 - 20 Apr 2022

Cited by 1 | Viewed by 2489

Abstract

As a developing country, Vietnam has experienced rapid economic development since the 21st century. It is therefore becoming increasingly important to understand its spatial pattern of urban expansion. However, the key challenge in this endeavor lies in the lack of national accounting data [...] Read more.

As a developing country, Vietnam has experienced rapid economic development since the 21st century. It is therefore becoming increasingly important to understand its spatial pattern of urban expansion. However, the key challenge in this endeavor lies in the lack of national accounting data for the sub-administrative divisions of Vietnam at the national level, especially longitudinal data over a long time series. The nighttime lights (NTL) data can objectively reflect the scope and intensity of human development and construction in urban built-up areas, which can effectively support the empirical analysis of the urban expansion process in Vietnam. This paper uses the intercalibration model to correct and fit the long time series of DMSP/OLS and VIIRS/NPP NTL data. Based on this, the data for the urban built areas of Vietnam from 2000 to 2018 are further extracted. The results are as follows. (1) The main urban expansion in Vietnam is concentrated in the southern Mekong Delta and the northern Red River Delta, represented by Ho Chi Minh City and Hanoi City, respectively. (2) Vietnam’s urban NTL has significant high–high clustering characteristics in the north-south delta regions. The main urban expansion hotspots were concentrated around Ho Chi Minh City before 2012, the northern region represented by Hanoi City was gradually transformed into a critical area that gathering urban expansion hotspots after 2012. (3) The cities with significant influence and high coupling degree of industrialization and globalization on the urbanization of Vietnam are concentrated in Ho Chi Minh City, Hanoi, and some northern delta provinces, showing that the impact of industrialization and globalization on urbanization in Vietnam is still limited to some regions. In addition, the results show that the size of the population and the level of industrialization are the main drivers of urban expansion in Vietnam, while the level of foreign investment shows little significance. The results are helpful for promoting the application of long time series NTL data in urban expansion and for further analyzing the urban pattern changes in Vietnam and the influencing factors behind them. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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18 pages, 3795 KiB  

Open AccessArticle

Waste Management 4.0: An Application of a Machine Learning Model to Identify and Measure Household Waste Contamination—A Case Study in Australia

by Atiq Zaman

Sustainability 2022, 14(5), 3061; https://0-doi-org.brum.beds.ac.uk/10.3390/su14053061 - 06 Mar 2022

Cited by 11 | Viewed by 4633

Abstract

Waste management directly and indirectly contributes to all sustainable development goals. Hence, the modernisation of the current ineffective management system through Industry 4.0-compatible technologies is urgently needed. Inspired by the fourth industrial revaluation, this study explores the potential application of waste management 4.0 [...] Read more.

Waste management directly and indirectly contributes to all sustainable development goals. Hence, the modernisation of the current ineffective management system through Industry 4.0-compatible technologies is urgently needed. Inspired by the fourth industrial revaluation, this study explores the potential application of waste management 4.0 in a local government area in Perth, Western Australia. The study considers a systematic literature review as part of an exploratory investigation of the current applications and practices of Industry 4.0 in the waste industry. Moreover, the study develops and tests a machine learning model to identify and measure household waste contamination as a waste management 4.0 case study application. The study reveals that waste management 4.0 offers various opportunities and sustainability benefits in reducing costs, improving efficiency in the supply chain and material flow, and reducing as well as eliminating waste by achieving holistic circular economy goals. The significant barriers and challenges involve initial investments in developing and maintaining waste management 4.0 technology, platform and data acquisition. The proof-of-concept case study on the machine learning model detects selected waste with considerable precision (over 70% for selected items). The number and quality of the labelled data significantly influences the model’s accuracy. The data on waste contamination are essential for local governments to explore household waste recycling practices besides developing effective waste education and communication methods. The study concludes that waste management 4.0 can be an effective tool for acquiring real-time data; however, overcoming the current limitations needs to be addressed before applying waste management 4.0 into practice. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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18 pages, 2624 KiB  

Open AccessArticle

A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments

by Thomas Larm, Anna Wahlsten, Jiri Marsalek and Maria Viklander

Sustainability 2022, 14(5), 2888; https://0-doi-org.brum.beds.ac.uk/10.3390/su14052888 - 02 Mar 2022

Cited by 2 | Viewed by 1791

Abstract

The StormTac Web model, representing a low-complexity conceptual model (LCCM), was applied to two urban catchments featuring stormwater quality controls, a stormwater pond or a biofilter. The model calculates annual average runoff from annual precipitation and land-use specific volumetric runoff coefficients and baseflows [...] Read more.

The StormTac Web model, representing a low-complexity conceptual model (LCCM), was applied to two urban catchments featuring stormwater quality controls, a stormwater pond or a biofilter. The model calculates annual average runoff from annual precipitation and land-use specific volumetric runoff coefficients and baseflows (in storm sewers), which are multiplied by the corresponding mean stormwater quality constituent concentrations obtained from the recently upgraded StormTac Database, to yield constituent loads. The resulting runoff loads pass through the stormwater quality control facilities (a stormwater pond or a biofilter) where treatment takes place and its efficacy is described by “reduction efficiencies”. For the four selected stormwater quality constituents (TP, Cu, Zn, TSS) and two study catchments, a 201-ha residential Ladbrodammen and an 8.2-ha Sundsvall traffic corridor, the compositions of stormwater entering and leaving the control facilities were calculated by StormTac Web and compared against the measured data. In general, the calculated concentrations were smaller than the measured ones, and these differences were reduced, but not eliminated in all cases, by considering uncertainties in both calculated and measured data. Uncertainties in calculated values consisted of two components, a flow component (assumed as 20%) and a concentration component, which was assumed equal to the relative standard error (RSE) of the data in the StormTac Database. Explanations of differences in calculated and measured stormwater data were discussed with respect to temporal changes and trends in environmental practices and stormwater quality monitoring and enhancement by treatment. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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18 pages, 11694 KiB  

Open AccessArticle

Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data

by Minmin Li, Wenhua Guo, Renzhong Guo, Biao He, Zhichao Li, Xiaoming Li, Wenchao Liu and Yong Fan

Land 2022, 11(2), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/land11020225 - 02 Feb 2022

Cited by 5 | Viewed by 2831

Abstract

China’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within [...] Read more.

China’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within and between regions of different scales. The existing research only carries out the characteristic analysis of urban network spatial connection and pattern from a single perspective such as “flow space” theory, spatial interaction model and accessibility method, and fails to accurately describe the complex socio-economic relations between regions. Based on the big data of railway passenger flow, this study selected weighted average travel time, railway network density, and the economic connection model to express the urban network spatial connection and structure of China in 2016 from the perspectives of time, space, and interaction. In 2016, the accessibility, connectivity, and total urban external economic connection of the railway network showed a trend of declining from the east to the west. The top 50 cities ranked by interurban economic connection were all located in the central and eastern regions and showed “diamond shape” distribution characteristics. The four diamond-shaped pairs were Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengyu urban agglomerations. This shape was basically in line with the T-shaped space that has existed for a long time in China’s regional development. The accessibility, connectivity, and total external economic connection of national-level urban agglomerations were greater than those of regional-level urban agglomerations, and far greater than those of local-level urban agglomerations. The results showed that there was a mismatch between the layout of the railway network and the population. It will still be necessary to focus on strengthening the construction of transportation infrastructure in urban agglomerations and densely populated areas in the future. This study enriches the “flow space” theory, more fully describes urban network spatial connection and structure in China by considering the three perspectives of time, space, and interaction, and can provides reasonable suggestions for the development of national comprehensive three-dimensional transportation network planning, regional spatial structure optimization, and sustainable development. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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17 pages, 9563 KiB  

Open AccessArticle

A Comparative Study on Planning Patterns of Industrial Bases in Northeast China Based on Spatial Syntax

by Rui Han, Daping Liu, Guangjie Zhu and Linjie Li

Sustainability 2022, 14(2), 1041; https://0-doi-org.brum.beds.ac.uk/10.3390/su14021041 - 17 Jan 2022

Cited by 3 | Viewed by 1760

Abstract

After World War II, unprecedented and positive industrialization and urban construction were launched in lots of developing countries all over the world. Meanwhile, more and more far-reaching planning theories and technological achievements emerged. In this study, we combed the development process of the [...] Read more.

After World War II, unprecedented and positive industrialization and urban construction were launched in lots of developing countries all over the world. Meanwhile, more and more far-reaching planning theories and technological achievements emerged. In this study, we combed the development process of the industrial base planning pattern created by the Soviet Union in the 1950s, summarized its main theoretical and technical contents and its transfer to Northeast China, and revealed the absorption and innovation of this planning pattern in three import industrial cities built in the 1950s in Northeast China. Based on the spatial syntax theory and technology, the practice of three representative industrial bases’ planning patterns was deeply analyzed. A comparative study on the theoretical and technical fit planning level among the three bases was implemented from the two aspects of the extension of different functional spatial modules and the connection and accessibility of the road axis. It was finally found that the planning pattern of the new industrial base of a single plant had more advantages of functional support and road accessibility in spatial morphology. The conclusion of this study not only generated great historical value for combing the history of contemporary industrial urban planning in China but is also a significant reference for the sustainable development of the industrial cities in Northeast China. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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15 pages, 1495 KiB  

Open AccessArticle

The Trade-Offs between Supply and Demand Dynamics of Ecosystem Services in the Bay Areas of Metropolitan Regions: A Case Study in Quanzhou, China

by Wei Shui, Kexin Wu, Yong Du and Haifeng Yang

Land 2022, 11(1), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/land11010022 - 23 Dec 2021

Cited by 5 | Viewed by 2695

Abstract

Bay areas are endowed with unique sea and land resources, location advantages, and high environmental carrying capacities. The rapid urbanization process has intensified the demand for limited natural resources, leading to a series of problems in coastal zones such as land use conflicts [...] Read more.

Bay areas are endowed with unique sea and land resources, location advantages, and high environmental carrying capacities. The rapid urbanization process has intensified the demand for limited natural resources, leading to a series of problems in coastal zones such as land use conflicts and the degradation of ecosystem services. Taking Quanzhou, a bay city in a metropolitan region, as an example, this paper established an accounting model of ecosystem services supply and consumption demand based on multisource data (meteorological site data, land use data and statistical data). We estimated the supply capacity and consumption demand of provisioning services, regulating services, and cultural services in Quanzhou from 2005 to 2015. In addition, the supply and demand of ecosystem services were simulated for 2030 under different scenarios. The results showed that the supply capacity of ecosystem services in Quanzhou was greater than the demand in general, but the supply-demand difference showed a gradual decrease. The high-value areas of supply capacity were concentrated in the upstream basin in the non-bay area, while the high-value areas of consumption demand were located downstream of the river basin in the bay area. The supply-demand difference in the bay area was negative, indicating that it was in a state of supply-demand imbalance and that the ecological security was under threat. Among the three simulated scenarios in 2030, the balance between supply and demand declined compared with the results of 2015, with the most serious decline in the natural scenario. The method to quantify the evolution of spatial and temporal patterns in supply and demand of ecosystem services could provide a decision-making reference for natural resource management in Quanzhou. This is conducive to the improvement and establishment of urban ecological security research systems, especially in bay areas that are lacking research. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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20 pages, 37932 KiB  

Open AccessArticle

Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

by Boce Chu, Feng Gao, Yingte Chai, Yu Liu, Chen Yao, Jinyong Chen, Shicheng Wang, Feng Li and Chao Zhang

Sustainability 2021, 13(23), 13475; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313475 - 06 Dec 2021

Cited by 2 | Viewed by 1686

Abstract

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks [...] Read more.

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect. Full article

(This article belongs to the Topic Urban Computing—Data, Techniques, Tools, and Applications)

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