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Geographic Big Data Analysis and Urban Sustainable Development

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 12737

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Special Issue Editors

Dr. Jianbo Tang

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Guest Editor

School of Geosciences and Info-physics, Central South University, Changsha 410012, China
Interests: spatiotemporal data mining; crowdsourcing mapping; geographic data analysis and applications; urban computing

Dr. Wentao Yang

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Guest Editor

National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Interests: spatiotemporal modeling and forecasting; urban and ecological environment assessment; GIS applications

Dr. Xuexi Yang

E-Mail Website
Guest Editor

School of Geosciences and Info-physics, Central South University, Changsha 410012, China
Interests: spatiotemporal data mining; spatial Interaction; social sensing; geographic big data analysis

Special Issue Information

Dear Colleagues,

With the development of urbanization and population growth, many cities are faced with urban problems such as population agglomeration, traffic congestion, environmental and air pollution, unbalanced distribution of urban infrastructures, and unsustainable land development and resources utilization, which seriously restrict the healthy and sustainable development of cities. In order to solve the problems faced by sustainable urban development, we need to find out the current development status of the city, such as the distribution of natural resources, their social and economic centers, their and population distribution and human mobility patterns. Their changes in space time must be dynamically perceived and monitored to analyze and predict their future development trends and to formulate scientific and reasonable urban planning and management plans to achieve a sustainable urban development. Geographic information system/science (GIS) includes important theoretical methods and technologies for analyzing and modeling the spatial and temporal evolution of elements, geographical phenomena or processes in urban space. GIS has been widely used in urban planning, traffic control, ecological and environmental monitoring and evaluation, population distribution and mobility pattern analysis. Especially in the context of the era of big data, with the development of earth observation technologies, sensors, and communication technology, everyone can be a sensor of the urban space and environment, generating massive multi-source geographic big data, such as vehicle GPS trajectory data, mobile phone positioning data, social media check-in data, POI data, street view images and video data, etc. These geographic big data provide new opportunities for the analysis of urban environments, social economy, infrastructure and resource distribution, dynamic population and traffic. How to make full use of and excavate the information and knowledge contained in these geographic big data to help solve the problems faced in the sustainable development of cities is still an open topic. Thus, ideas, methods, systems and practical applications of GIS, enabling geographic big data for urban studies, will be welcome in this Special Issue.

This Special Issue aims to encourage researchers to publish their new ideas, models, methods and frameworks on the analysis of geographic big data and applications in urban sustainable development, so as to promote the usage of geographic big data and GIS technologies in sustainable urban studies. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Quality evaluation of geographic big data;
Multi-source heterogeneous spatiotemporal data fusion;
Geographic big data mining and knowledge discovery;
Dynamic population mapping;
Forecast and health risk assessment of air pollution;
Urban land use/functional zones change detection;
Urban road network mapping and traffic analysis;
Human mobility patterns and spatial interaction;
Social sensing for urban planning and management;
Applications of GIS for urban sustainable development.

We look forward to receiving your contributions.

Dr. Jianbo Tang
Dr. Wentao Yang
Dr. Xuexi Yang
Guest Editors

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Keywords

geographic big data
multi-source data
data mining
urban planning and management
social sensing

Published Papers (8 papers)

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Research

24 pages, 8428 KiB

Open AccessArticle

Short-Term PM_2.5 Concentration Changes Prediction: A Comparison of Meteorological and Historical Data

by Junfeng Kang, Xinyi Zou, Jianlin Tan, Jun Li and Hamed Karimian

Sustainability 2023, 15(14), 11408; https://doi.org/10.3390/su151411408 - 22 Jul 2023

Cited by 2 | Viewed by 1101

Abstract

Machine learning is being extensively employed in the prediction of PM_2.5 concentrations. This study aims to compare the prediction accuracy of machine learning models for short-term PM_2.5 concentration changes and to find a universal and robust model for both hourly and daily time scales. Five commonly used machine learning models were constructed, along with a stacking model consisting of Multivariable Linear Regression (MLR) as the meta-learner and the ensemble of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as the base learner models. The meteorological datasets and historical PM_2.5 concentration data with meteorological datasets were preprocessed and used to evaluate the model’s accuracy and stability across different time scales, including hourly and daily, using the coefficient of determination (R²), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE). The results show that historical PM_2.5 concentration data are crucial for the prediction precision of the machine learning models. Specifically, on the meteorological datasets, the stacking model, XGboost, and RF had better performance for hourly prediction, and the stacking model, XGboost and LightGBM had better performance for daily prediction. On the historical PM_2.5 concentration data with meteorological datasets, the stacking model, LightGBM, and XGboost had better performance for hourly and daily datasets. Consequently, the stacking model outperformed individual models, with the XGBoost model being the best individual model to predict the PM_2.5 concentration based on meteorological data, and the LightGBM model being the best individual model to predict the PM_2.5 concentration using historical PM_2.5 data with meteorological datasets. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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18 pages, 3750 KiB

Open AccessArticle

Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

by Fuxun Chen, Lanxin Zhang, Siyu Kang, Lutong Chen, Honghong Dong, Dan Li and Xiaozhu Wu

Sustainability 2023, 15(14), 10751; https://doi.org/10.3390/su151410751 - 8 Jul 2023

Cited by 7 | Viewed by 1625

Abstract

In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection of small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge due to the limited pixel values and interference from background noise. To address this challenge, this paper proposes an integrated object detection approach that utilizes an improved YOLOv5 model for real-time detection of small water surface floaters. The proposed improved YOLOv5 model effectively detects small objects by better integrating shallow and deep features and addressing the issue of missed detections and, therefore, aligns with the characteristics of the water surface floater dataset. Our proposed model has demonstrated significant improvements in detecting small water surface floaters when compared to previous studies. Specifically, the average precision (AP), recall (R), and frames per second (FPS) of our model achieved 86.3%, 79.4%, and 92%, respectively. Furthermore, when compared to the original YOLOv5 model, our model exhibits a notable increase in both AP and R, with improvements of 5% and 6.1%, respectively. As such, the proposed improved YOLOv5 model is well-suited for the real-time detection of small objects on the water’s surface. Therefore, this method will be essential for large-scale, high-precision, and intelligent water surface floater monitoring. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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23 pages, 20233 KiB

Open AccessArticle

Identification of Urban Functional Areas and Urban Spatial Structure Analysis by Fusing Multi-Source Data Features: A Case Study of Zhengzhou, China

by Jinxin Wang, Chaoran Gao, Manman Wang and Yan Zhang

Sustainability 2023, 15(8), 6505; https://doi.org/10.3390/su15086505 - 11 Apr 2023

Cited by 2 | Viewed by 2114

Abstract

The identification and delineation of urban functional zones (UFZs), which are the basic units of urban organisms, are crucial for understanding complex urban systems and the rational allocation and management of resources. Points of interest (POI) data are weak in identifying UFZs in areas with low building density and sparse data, whereas remote sensing data lack the necessary semantic information for functional zoning, and single-source data cannot perform a highly comprehensive characterization of complex UFZs. To address these issues, this study proposes a method for identifying UFZs by fusing multi-attribute features from multi-source data and introduces nighttime light and land surface temperature (LST) indicators as functional zoning references, taking the main urban area of Zhengzhou as an example. The experimental results show that the POI data with integrated three-level semantic information can characterize the semantic information of functional areas well, and the incorporation of multi-spectral, nighttime light, and LST data can further improve the recognition accuracy by approximately 10.1% compared with the POI single-source data. The final recognition accuracy and kappa coefficient reached 84.00% and 0.8162, respectively, indicating that the method is largely consistent with the actual situation and is feasible. The analysis showed that the main urban area of Zhengzhou as a whole is characterized by the coordinated development of single and mixed functional areas, in which a distinct residential-commercial-public complex is formed, and the urban functional areas on the block scale have diverse attributes. This study can provide a decision-making reference for the future development planning and management of Zhengzhou, China. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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19 pages, 2594 KiB

Open AccessArticle

Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors

by Liping Du, Xianghong Zhou, Ruting Yang, Pengfei Cheng and Sijie Cheng

Sustainability 2023, 15(7), 6046; https://doi.org/10.3390/su15076046 - 31 Mar 2023

Viewed by 1226

Abstract

In recent years, environmental pollution and massive consumption of resources in the traditional development model have posed significant challenges to the environment and society. In this study, we discuss the influencing factors of high-quality development. High-quality development is increasingly important to exploring the current state of quality development in China. Using the evaluation data of the major nodes of China’s provincial administrative regions from 2007 to 2019, the entropy value method was applied to calculate the comprehensive index of high-quality regional development, explain the spatial and temporal evolution pattern of China’s high-quality regional development, and reveal the internal driving factors of spatial and temporal evolution. The study concludes that the level of high-quality regional development in the east is higher than that in the western region, and the level of high-quality regional development in the southern region is higher than that in the north. Investment influence, industrial development, and urban–rural development have positive effects on high-quality regional development. However, location and transportation negatively affect high-quality regional development. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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21 pages, 1337 KiB

Open AccessArticle

A Study on the Policy Effects of the Establishment of Guangdong–Hong Kong–Macao Greater Bay Area on Logistics Efficiency

by Shuquan Hong, Huiyuan Jiang, Shuang Cheng, Yuejun Huang and Chao Feng

Sustainability 2023, 15(2), 1078; https://doi.org/10.3390/su15021078 - 6 Jan 2023

Cited by 1 | Viewed by 1397

Abstract

Logistics efficiency is an important indicator when measuring the level of development of the logistics industry, and policy factors are the most difficult to measure among the factors affecting logistics efficiency. This study aimed to construct a new empirical model by combining a three-stage data envelopment analysis (DEA) model and the econometric method propensity score matching and difference-in-differences (PSM–DID) to measure and analyze the net change in logistics efficiency in the Guangdong–Hong Kong–Macao Greater Bay Area under the influence of this policy factor. The empirical evidence shows that different amounts of change occurred in the two time periods after the establishment of the Greater Bay Area and a significant increase in logistics efficiency occurred in the second period, further demonstrating that the economic policy of the Greater Bay Area is effective in improving logistics efficiency and providing a case reference for other countries or regions with similar conditions. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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20 pages, 6097 KiB

Open AccessArticle

Channel Evolution under the Control of Base-Level Cycle Change and the Influence on the Sustainable Development of the Remaining Oil—A Case in Jiang Ling Depression, Jiang Han Basin, China

by Wei Zhu, Mingsu Shen, Shixin Dai, Kuanning Liu and Yongdi Qi

Sustainability 2022, 14(19), 12518; https://doi.org/10.3390/su141912518 - 30 Sep 2022

Cited by 2 | Viewed by 1133

Abstract

The extension of river channels is one of the key factors in determining the remaining oil distribution. Different sedimentary facies and bedding types of oil layers will form specific characteristics of remaining oil distribution after water injection development. Using massive drilling, core, logging, seismic, and production data, on the basis of sequence stratigraphy base-level cycle change, the river records and development history are restored, and the fine connectivity of reservoirs and the configuration relationship of production wells are studied. The following conclusions are drawn: (1) A sequence stratigraphic division scheme is established. In the established sequence framework, the types and characteristics of reservoir sand bodies are analyzed. The 2nd and 6th members of Yu yang formation can be divided into 2 long-term base level cycles, 5 medium-term base level cycles, and 17 short-term base level cycles. The evolution of the second and sixth members of the Yu yang formation shows a pattern of base level rising, falling and rising again; (2) the vertical sedimentary evolution sequence is underwater distributary channel distributary channel meandering channel distributary channel flood plain. The types of channel sand bodies developed from little overlap to more vertical or lateral overlap and then gradually changed to isolated type; (3) according to the structural location and development sequence, different types of reservoirs are identified. Combined with the statistics of the drilled data of Yu yang formation k₂y4 in Fu I fault block, it is found that the connectivity rate of oil layer thickness (the ratio of oil layer connectivity thickness to total thickness of sand layer) within the oil-bearing area is 84.4%, and the connectivity rate of the number of layers (8) is 60%. The connectivity condition is relatively good. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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18 pages, 6567 KiB

Open AccessArticle

Uncovering the Structural Effect Mechanisms of Natural and Social Factors on Land Subsidence: A Case Study in Beijing

by Bin Zhao, Xuexi Yang, Qianhong Wu, Weifeng Xiao, Wentao Yang and Min Deng

Sustainability 2022, 14(16), 10139; https://doi.org/10.3390/su141610139 - 16 Aug 2022

Viewed by 1225

Abstract

Understanding the effect mechanisms of various factors on land subsidence may help in the development of scientific measures to control land subsidence. Previous studies mainly focused on exploring local effect mechanisms, such as extracting hotspots and analyzing their spatiotemporal distribution characteristics and identifying the interaction mechanisms of the associated factors. However, the scarcely discussed structural effect mechanisms on a small scale suggests a need to further explore the effects on land subsidence. Therefore, in this paper, an analytical framework was proposed to elaborate the structural effect mechanisms of influencing factors on land subsidence. First, the local effect mechanisms were identified using the geographically and temporally weighted regression (GTWR) model, followed by a spatial clustering analysis and the detection of their aggregation pattern using the spatially constrained multivariate clustering (SCMC) model to show the structural mechanisms. Study datasets included the monitoring results of land subsidence during 2003–2010 and the related socioeconomic factors by using synthetic aperture radar (SAR) images from Beijing. Factors such as population, annual average rainfall, underground water, and static load were identified to measure the changes in land subsidence, and all of these had both negative and positive impacts. Among these, the annual average rainfall had the largest coefficient variation range. These four geographically associated factors revealed various spatiotemporal effects on land subsidence in Beijing, showing land subsidence changes resulting from the urbanization process of Beijing during that period. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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13 pages, 1742 KiB

Open AccessArticle

Coupling Analysis of the Road-Network Spatiotemporal Distribution and the Economy in B&R Countries Based on GIS

by Yao Tong, Cui Zhou, Jingying Lin, Chengkai Tan and Wenjian Tang

Sustainability 2022, 14(14), 8419; https://doi.org/10.3390/su14148419 - 9 Jul 2022

Viewed by 1216

Abstract

The Belt and Road (B&R) is a new strategy and measure for China to extend its opening up. To explore the influence of the spatiotemporal distribution of the national road network along the B&R on economic growth, this paper adopts the subjective and objective integrated weighting method to build a regional economic evaluation model, a transportation network evaluation model, and an economy–transportation coupling coordination degree model (E-T model). We also quantitatively analyze and evaluate the coordinated development of the economy and transportation in the countries along the B&R. Our results show that: (1) There are some differences in the comprehensive scores of economic level and transportation network in different countries, and the B&R has promoted the general economic and transportation level of various countries. (2) Approximately 84% of the countries have not reached a good coordination level, and the regional differences are significant, which indicates that the overall economic and transportation coupling coordination needs to be improved. (3) In recent years, driven by the B&R, the coupling coordination of approximately 30% of the countries has improved significantly. Therefore, the B&R not only has a positive impact on the economy and transportation of countries along the belt but also plays an important role in coordinating the economic and transportation development of countries, which is of great strategic significance. Full article

(This article belongs to the Special Issue Geographic Big Data Analysis and Urban Sustainable Development)

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