ISPRS International Journal of Geo-Information

18 pages, 3966 KiB

Open AccessArticle

Forecasting Short-Term Passenger Flow of Subway Stations Based on the Temporal Pattern Attention Mechanism and the Long Short-Term Memory Network

by Lingxiang Wei, Dongjun Guo, Zhilong Chen, Jincheng Yang and Tianliu Feng

ISPRS Int. J. Geo-Inf. 2023, 12(1), 25; https://doi.org/10.3390/ijgi12010025 - 16 Jan 2023

Cited by 6 | Viewed by 2717

Abstract

Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and [...] Read more.

Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents. Full article

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17 pages, 9587 KiB

Open AccessArticle

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

by Song Chen, Fuhao Zhang, Zhiran Zhang, Siyi Yu, Agen Qiu, Shangqin Liu and Xizhi Zhao

ISPRS Int. J. Geo-Inf. 2023, 12(1), 24; https://doi.org/10.3390/ijgi12010024 - 14 Jan 2023

Cited by 1 | Viewed by 1990

Abstract

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called [...] Read more.

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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22 pages, 20309 KiB

Open AccessArticle

Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic

by Rui Xin, Linfang Ding, Bo Ai, Min Yang, Ruoxin Zhu, Bin Cao and Liqiu Meng

ISPRS Int. J. Geo-Inf. 2023, 12(1), 23; https://doi.org/10.3390/ijgi12010023 - 13 Jan 2023

Viewed by 2150

Abstract

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of [...] Read more.

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings. Full article

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19 pages, 4799 KiB

Open AccessArticle

Modeling the Impact of Investment and National Planning Policies on Future Land Use Development: A Case Study for Myanmar

by Yuan Jin, Ainong Li, Jinhu Bian, Xi Nan and Guangbin Lei

ISPRS Int. J. Geo-Inf. 2023, 12(1), 22; https://doi.org/10.3390/ijgi12010022 - 13 Jan 2023

Cited by 5 | Viewed by 2194

Abstract

Land use change (LUC) can be affected by investment growth and planning policies under the context of regional economic cooperation and development. Previous studies on land use simulation mostly emphasized the effects of local socioeconomic factors and planning constraint areas that prevent land [...] Read more.

Land use change (LUC) can be affected by investment growth and planning policies under the context of regional economic cooperation and development. Previous studies on land use simulation mostly emphasized the effects of local socioeconomic factors and planning constraint areas that prevent land conversions. However, investment and national planning policies that trigger regional LUC were often ignored. This study aims to couple the economic theory-based Computable General Equilibrium of Land Use Change (CGELUC) model and the cellular automata-based Future Land Use Simulation (FLUS) model to incorporate macroscopic impacts of investment into land use simulation, while proposing an updated mechanism that integrates into the FLUS model to consider the local impacts of planning policies. Taking Myanmar as a case, the method was applied to project the land use patterns (LUPs) during 2017–2050 under three scenarios: baseline, fast, and harmonious development. Specifically, the simulated land use structure (LUS) in 2018 acquired by the CGELUC model was verified by the existing data, and the future LUSs under different scenarios were projected later. Simultaneously, the consistencies between the results simulated by the FLUS model and land use maps in 2013, 2015, and 2017 were represented by the kappa coefficient. The updated mechanism was applied to update the Probability-of-Occurrence (PoO) surfaces based on the planning railway networks and special economic zone. Lastly, the LUPs under different scenarios were projected based on the future LUSs and updated PoO surfaces. Results reveal that the validation accuracy reaches 96.87% for the simulated LUS, and satisfactory accuracies of the simulated LUPs are obtained (kappa coefficients > 0.83). The updated mechanism increases the mean PoO values of built-up land in areas affected by planning policies (increasing by 0.01 to 0.21), indicating the importance of the planning policies in simulation. The cultivated land and built-up land increase with investment increasing under all three scenarios. The harmonious development scenario, showing the least forest encroachment and the highest diversity of LUP, is the optimal approach to achieve land sustainability. This study highlights the impacts of investment and planning policies on future LUCs of Myanmar, and a dynamic simulation process is expected to minimize the uncertainties of the input data and model in the future work. Full article

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26 pages, 8470 KiB

Open AccessArticle

Visual Attention and Recognition Differences Based on Expertise in a Map Reading and Memorability Study

by Merve Keskin, Vassilios Krassanakis and Arzu Çöltekin

ISPRS Int. J. Geo-Inf. 2023, 12(1), 21; https://doi.org/10.3390/ijgi12010021 - 12 Jan 2023

Cited by 2 | Viewed by 2844

Abstract

This study investigates how expert and novice map users’ attention is influenced by the map design characteristics of 2D web maps by building and sharing a framework to analyze large volumes of eye tracking data. Our goal is to respond to the following [...] Read more.

This study investigates how expert and novice map users’ attention is influenced by the map design characteristics of 2D web maps by building and sharing a framework to analyze large volumes of eye tracking data. Our goal is to respond to the following research questions: (i) which map landmarks are easily remembered? (memorability), (ii) how are task difficulty and recognition performance associated? (task difficulty), and (iii) how do experts and novices differ in terms of recognition performance? (expertise). In this context, we developed an automated area-of-interest (AOI) analysis framework to evaluate participants’ fixation durations, and to assess the influence of linear and polygonal map features on spatial memory. Our results demonstrate task-relevant attention patterns by all participants, and better selective attention allocation by experts. However, overall, we observe that task type and map feature type mattered more than expertise when remembering the map content. Predominantly polygonal map features such as hydrographic areas and road junctions serve as attentive features in terms of map reading and memorability. We make our dataset entitled CartoGAZE publicly available. Full article

(This article belongs to the Special Issue Eye-Tracking in Cartography)

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22 pages, 1588 KiB

Open AccessArticle

Modeling Land Administration Data Dissemination Processes: A Case Study in Croatia

by Josip Križanović and Miodrag Roić

ISPRS Int. J. Geo-Inf. 2023, 12(1), 20; https://doi.org/10.3390/ijgi12010020 - 12 Jan 2023

Cited by 2 | Viewed by 1661

Abstract

Establishing land administration systems is enough of a challenge as it is, and the task of keeping the system up to date with developments in society is even more challenging. They have to serve society on a long-term basis and normally have a [...] Read more.

Establishing land administration systems is enough of a challenge as it is, and the task of keeping the system up to date with developments in society is even more challenging. They have to serve society on a long-term basis and normally have a long-term return on investment; therefore, both the static and dynamic components of the system must be considered when designing land administration systems. The processes within land administration systems are registration and dissemination. In this study, the authors formalized and analyzed the two most common use cases of land administration data dissemination processes. The first use case depicts the dissemination of land use constraints imposed by spatial planning, whereas the second case depicts the dissemination of available utilities. The aim of this study was to examine how the land administration data dissemination processes could be optimized and improved in a standardized formal manner. From the formalized processes, certain elements, such as actors, activities, input and output data, and the timeframe, were identified and matched with existing LADM classes. The importance of institutional agreements and the need for more time-efficient and user-friendly access to the disseminated data are also discussed in the current paper. Full article

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13 pages, 2209 KiB

Open AccessEditor’s ChoiceArticle

Automatic Clustering of Indoor Area Features in Shopping Malls

by Ziren Gao, Yi Shen, Jingsong Ma, Jie Shen and Jing Zheng

ISPRS Int. J. Geo-Inf. 2023, 12(1), 19; https://doi.org/10.3390/ijgi12010019 - 10 Jan 2023

Viewed by 1292

Abstract

The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map [...] Read more.

The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map area features as generalization objects and deems the automatic clustering of the indoor area features of shopping malls as the research goal. The approach is used to construct an encoder-decoder clustering model, where the encoder consists of a graph convolutional network and its variant models. The results show that the proposed model framework effectively extracts the area features suitable for the indoor space clustering of shopping malls and improves clustering efficacy. Specifically, the model with the Relational Graph Convolutional Network as the encoder demonstrated the best performance, time complexity, and accuracy of clustering results, with accuracy up to 95%. This study extends the research object of cartographic generalization to indoor maps, enabling the automatic clustering of indoor area features, and proposes a clustering model for the important indoor scene of shopping malls. This is valuable for scholars interested in the cartographic generalization of indoor maps. Full article

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15 pages, 3962 KiB

Open AccessArticle

Measuring Metro Accessibility: An Exploratory Study of Wuhan Based on Multi-Source Urban Data

by Tao Wu, Mingjing Li and Ye Zhou

ISPRS Int. J. Geo-Inf. 2023, 12(1), 18; https://doi.org/10.3390/ijgi12010018 - 10 Jan 2023

Cited by 4 | Viewed by 2075

Abstract

Metro accessibility has attracted interest in sustainable transport analyses. Hence, the accuracy of metro-accessibility measures have become increasingly vital. Various spatiotemporal factors, including by-metro accessibility, land-use accessibility and to-metro accessibility, affect metro accessibility; however, measuring metro accessibility while considering all these components simultaneously [...] Read more.

Metro accessibility has attracted interest in sustainable transport analyses. Hence, the accuracy of metro-accessibility measures have become increasingly vital. Various spatiotemporal factors, including by-metro accessibility, land-use accessibility and to-metro accessibility, affect metro accessibility; however, measuring metro accessibility while considering all these components simultaneously is challenging. By integrating these factors into a unified analysis framework, this study aims to strengthen the method for metro-accessibility assessment. Specifically, we proposed the “By metro–Land use–To metro” model to conduct a metro-accessibility index and develop an accessibility-based station typology. The results show that Wuhan metro system accessibility presented a “high-medium-low” spatial disparity from the urban center to the periphery. Meanwhile, the variety of metro-accessibility characteristics and typologies in Wuhan will equip urban planners and policymakers with a useful tool for better organising by-metro accessibility, land-use accessibility and to-metro accessibility. Full article

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20 pages, 7764 KiB

Open AccessArticle

Evaluation of Geological Hazard Susceptibility Based on the Regional Division Information Value Method

by Jingru Ma, Xiaodong Wang and Guangxiang Yuan

ISPRS Int. J. Geo-Inf. 2023, 12(1), 17; https://doi.org/10.3390/ijgi12010017 - 10 Jan 2023

Cited by 6 | Viewed by 1986

Abstract

The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to [...] Read more.

The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation. Full article

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21 pages, 3713 KiB

Open AccessArticle

Explanatory Factors of Daily Mobility Patterns in Suburban Areas: Applications and Taxonomy of Two Metropolitan Corridors in Madrid Region

by Andrea Alonso, Andrés Monzón, Iago Aguiar and Alba Ramírez-Saiz

ISPRS Int. J. Geo-Inf. 2023, 12(1), 16; https://doi.org/10.3390/ijgi12010016 - 09 Jan 2023

Cited by 1 | Viewed by 2715

Abstract

Understanding the characteristics that shape mobility could help to achieve more sustainable transport systems. A considerable body of scientific studies tries to determine these characteristics at the urban level. However, there is a lack of studies analyzing those factors for the heterogeneous zones [...] Read more.

Understanding the characteristics that shape mobility could help to achieve more sustainable transport systems. A considerable body of scientific studies tries to determine these characteristics at the urban level. However, there is a lack of studies analyzing those factors for the heterogeneous zones existing in the suburbs of big cities. The study presented in this paper intends to fill this gap, in the context of two metropolitan corridors in the Madrid Region. Correlation analyses are used to examine how mobility patterns are affected by socioeconomic and urban form variables. Then, a cluster analysis is carried out to classify the types of zones we may find in the suburbs. Results show that the main characteristics leading towards higher car use are low urban density, few local activities, a high percentage of children, and a low percentage of seniors. As for the variable distance to the city center, it does not explain car use. Moreover, some remote areas have many walking trips. This is well understood in the cluster analysis; there are zones far away from the city center but that are dense and well provided for, which work as self-sufficient urban centers. Results reinforce the theories underlying polycentrism as a solution to the urban sprawl challenge. Full article

(This article belongs to the Topic Advances in Sustainable Communities, Neighborhoods and 15-Minute Cities-Theory, Methods and Techniques)

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23 pages, 5501 KiB

Open AccessEditor’s ChoiceArticle

Diagnosis and Planning Strategies for Quality of Urban Street Space Based on Street View Images

by Jiwu Wang, Yali Hu and Wuxihong Duolihong

ISPRS Int. J. Geo-Inf. 2023, 12(1), 15; https://doi.org/10.3390/ijgi12010015 - 07 Jan 2023

Cited by 3 | Viewed by 3006

Abstract

Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play [...] Read more.

Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play important roles in the high-quality development of urban spatial environments. However, traditional planning design and study methods, typically based on questionnaires, interviews, and on-site research, are inefficient and make it difficult to objectively and comprehensively grasp the overall construction characteristics and problems of urban street space in a large area, thus making it challenging to meet the needs of practical planning. Therefore, based on street view images, this study combined machine learning with an artificial audit to put forward a methodological framework for diagnosing the quality issues of street space. The Gongshu District of Hangzhou, China, was selected as a case study, and the diagnosis of quality problems for streets at different grades was achieved. The diagnosis results showed the current situation and problems of the selected area. Simultaneously, a series of targeted strategies for street spatial update planning was proposed to solve these problems. This diagnostic method, based on a combination of subjective and objective approaches, can be conducive to the precise and comprehensive identification of urban public spatial problems, which is expected to become an effective tool to assist in urban renewal and other planning decisions. Full article

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19 pages, 3885 KiB

Open AccessArticle

Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery

by Wuttichai Boonpook, Yumin Tan, Attawut Nardkulpat, Kritanai Torsri, Peerapong Torteeka, Patcharin Kamsing, Utane Sawangwit, Jose Pena and Montri Jainaen

ISPRS Int. J. Geo-Inf. 2023, 12(1), 14; https://doi.org/10.3390/ijgi12010014 - 07 Jan 2023

Cited by 10 | Viewed by 5039

Abstract

Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem [...] Read more.

Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1–Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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18 pages, 4374 KiB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

by Li Cai, Cong Sha, Jing He and Shaowen Yao

ISPRS Int. J. Geo-Inf. 2023, 12(1), 13; https://doi.org/10.3390/ijgi12010013 - 04 Jan 2023

Cited by 1 | Viewed by 1853

Abstract

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that [...] Read more.

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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23 pages, 1214 KiB

Open AccessReview

Spatial Decision Support Systems with Automated Machine Learning: A Review

by Richard Wen and Songnian Li

ISPRS Int. J. Geo-Inf. 2023, 12(1), 12; https://doi.org/10.3390/ijgi12010012 - 30 Dec 2022

Cited by 2 | Viewed by 4287

Abstract

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and [...] Read more.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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13 pages, 2296 KiB

Open AccessArticle

A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring

by Prasad Pathak, Pranav Pandya, Sharvari Shukla, Aamod Sane and Raja Sengupta

ISPRS Int. J. Geo-Inf. 2023, 12(1), 11; https://doi.org/10.3390/ijgi12010011 - 30 Dec 2022

Viewed by 2158

Abstract

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures [...] Read more.

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations. Full article

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6 pages, 209 KiB

Open AccessEditorial

Artificial Intelligence for Multisource Geospatial Information

by Gloria Bordogna and Cristiano Fugazza

ISPRS Int. J. Geo-Inf. 2023, 12(1), 10; https://doi.org/10.3390/ijgi12010010 - 30 Dec 2022

Viewed by 2105

Abstract

The term Geospatial Artificial Intelligence (GeoAI) is quite cumbersome, and it has no single, shared definition [...] Full article

(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)

23 pages, 8859 KiB

Open AccessArticle

Billion Tree Tsunami Forests Classification Using Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2 and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan

by Shabnam Mateen, Narissara Nuthammachot, Kuaanan Techato and Nasim Ullah

ISPRS Int. J. Geo-Inf. 2023, 12(1), 9; https://doi.org/10.3390/ijgi12010009 - 29 Dec 2022

Cited by 5 | Viewed by 2716

Abstract

In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the [...] Read more.

In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the utilization of remote sensing techniques and satellite image classification has not yet been done. Recently, the Sentinel-2 (S2) satellite has found much utilization in remote sensing and land cover classification. Sentinel-2 (S2) sensors provide freely available images with a spatial resolution of 10, 20 and 60 m. The higher classification accuracy is directly dependent on the higher spatial resolution of the images. This research aims to classify the land cover changes as a result of the Billion Tree plantation drive in the areas of our interest using Random Forest Classifier (RFA) and image fusion techniques applied to Sentinel-2 and Landsat-8 satellite images. A state-of-the-art, model-based image-sharpening technique was used to sharpen the lower resolution Sentinel-2 bands to 10 m. Then the RFA classifier was used to classify the sharpened images and an accuracy assessment was performed for the classified images of the years 2016, 2018, 2020 and 2022. Finally, ground data samples were collected using an unmanned aerial vehicle (UAV) drone and the classified image samples were compared with the real data collected for the year 2022. The real data ground samples were matched by more than 90% with the classified image samples. The overall classification accuracies [%] for the classified images were recorded as 92.87%, 90.79%, 90.27% and 93.02% for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Similarly, an overall Kappa hat classification was calculated as 0.87, 0.86, 0.83 and 0.84 for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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25 pages, 3491 KiB

Open AccessArticle

Dynamic Analysis of School Mobility Using Geolocation Web Technologies

by David Fernández-Arango, Francisco-Alberto Varela-García and Jorge López-Fernández

ISPRS Int. J. Geo-Inf. 2023, 12(1), 8; https://doi.org/10.3390/ijgi12010008 - 29 Dec 2022

Viewed by 1833

Abstract

Pedestrian travel represents one of the most complex forms of mobility owing to the numerous parameters that influence its analysis and the difficulty of acquiring accurate travel information. In addition, the vulnerability of its protagonists, especially in urban environments, in coexistence with other [...] Read more.

Pedestrian travel represents one of the most complex forms of mobility owing to the numerous parameters that influence its analysis and the difficulty of acquiring accurate travel information. In addition, the vulnerability of its protagonists, especially in urban environments, in coexistence with other types of transport, makes its study interesting. This paper proposes a web tool for use in geolocated surveys that allows the acquisition of georeferenced thematic information of interest for mobility studies. The analysis of different school routes from students’ homes to their respective schools has been proposed as a case study. This work covered a sample of 1883 students from 26 schools in Galicia (Spain), where population dispersion generates a particular type of mobility. We obtained relevant mobility data, such as the routes most traveled by students in their daily commute to school, the most efficient routes, the most used means of transport, or the exact location of various elements that hinder and dangerously affect students traveling these routes, such as sidewalks or crosswalks in poor condition, among others. Full article

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15 pages, 2885 KiB

Open AccessArticle

Correlation of Road Network Structure and Urban Mobility Intensity: An Exploratory Study Using Geo-Tagged Tweets

by Li Geng and Ke Zhang

ISPRS Int. J. Geo-Inf. 2023, 12(1), 7; https://doi.org/10.3390/ijgi12010007 - 28 Dec 2022

Viewed by 2422

Abstract

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban [...] Read more.

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around

0.6

) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation

ρ \approx 0.4

at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

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13 pages, 32113 KiB

Open AccessArticle

Canopy Assessment of Cycling Routes: Comparison of Videos from a Bicycle-Mounted Camera and GPS and Satellite Imagery

by Albert Bourassa, Philippe Apparicio, Jérémy Gelb and Geneviève Boisjoly

ISPRS Int. J. Geo-Inf. 2023, 12(1), 6; https://doi.org/10.3390/ijgi12010006 - 27 Dec 2022

Cited by 1 | Viewed by 2640

Abstract

Many studies have proven that urban greenness is an important factor when cyclists choose a route. Thus, detecting trees along a cycling route is a major key to assessing the quality of cycling routes and providing further arguments to improve ridership and the [...] Read more.

Many studies have proven that urban greenness is an important factor when cyclists choose a route. Thus, detecting trees along a cycling route is a major key to assessing the quality of cycling routes and providing further arguments to improve ridership and the better design of cycling routes. The rise in the use of video recordings in data collection provides access to a new point of view of a city, with data recorded at eye level. This method may be superior to the commonly used normalized difference vegetation index (NDVI) from satellite imagery because satellite images are costly to obtain and cloud cover sometimes obscures the view. This study has two objectives: (1) to assess the number of trees along a cycling route using software object detection on videos, particularly the Detectron2 library, and (2) to compare the detected canopy on the videos to other canopy data to determine if they are comparable. Using bicycles installed with cameras and GPS, four participants cycled on 141 predefined routes in Montréal over 87 h for a total of 1199 km. More than 300,000 images were extracted and analyzed using Detectron2. The results show that the detection of trees using the software is accurate. Moreover, the comparison reveals a strong correlation (>0.75) between the two datasets. This means that the canopy data could be replaced by video-detected trees, which is particularly relevant in cities where open GIS data on street vegetation are not available. Full article

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18 pages, 4812 KiB

Open AccessArticle

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

by Yanzhong Yin, Qunyong Wu and Mengmeng Li

ISPRS Int. J. Geo-Inf. 2023, 12(1), 5; https://doi.org/10.3390/ijgi12010005 - 26 Dec 2022

Cited by 5 | Viewed by 2551

Abstract

Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts [...] Read more.

Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to measure intercity mobility characteristics. Next, we calculate the Normalized Levenshtein Distance for Population Mobility Structure (NLPMS) to quantify the differences in intercity mobility structures, and we use the Non-negative Matrix Factorization (NMF) to identify intercity mobility patterns. Our results showed an evident ‘Core-Periphery’ differentiation characterized by intercity mobility, with Guangzhou and Shenzhen as the two core cities. An obvious daily intercity commuting pattern was found between Guangzhou and Foshan, and between Shenzhen and Dongguan cities at working time. This pattern, however, changes during the holidays. This is because people move from the core cities to peripheral cities at the beginning of holidays and return at the end of holidays. This study concludes that Guangzhou and Foshan have formed a relatively stable intercity mobility pattern, and the Shenzhen–Dongguan–Huizhou metropolitan area has been gradually formed. Full article

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36 pages, 4991 KiB

Open AccessArticle

Recognition of Intersection Traffic Regulations from Crowdsourced Data

by Stefania Zourlidou, Monika Sester and Shaohan Hu

ISPRS Int. J. Geo-Inf. 2023, 12(1), 4; https://doi.org/10.3390/ijgi12010004 - 23 Dec 2022

Cited by 3 | Viewed by 2088

Abstract

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel [...] Read more.

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO

_{2}

emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%. Full article

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21 pages, 7721 KiB

Open AccessArticle

High-Speed Railway Access Pattern and Spatial Overlap Characteristics of the Yellow River Basin Urban Agglomeration

by Yajun Xiong, Hui Tang and Tao Xu

ISPRS Int. J. Geo-Inf. 2023, 12(1), 3; https://doi.org/10.3390/ijgi12010003 - 22 Dec 2022

Cited by 1 | Viewed by 1709

Abstract

With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR [...] Read more.

With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR access time in the Yellow River Basin to comparatively analyze the differences in HSR access in the urban agglomeration in the Yellow River Basin, and using the 3 h HSR access to central cities as the background to conduct regional division and overlapping space identification through cross-regional economic links, before finally selecting the overlapping city of Changzhi for long-term space development strategic planning. The main conclusions were as follows: First, the low-value area of HSR travel time in the Yellow River Basin urban agglomerations was biased toward the center of the urban agglomerations, while the peripheral areas were relatively high-value travel traffic circles, and the HSR travel time showed a circular spatial pattern characteristic of continuous expansion from the center to the peripheral areas. Four urban agglomerations in the upper reaches of the city achieved a 2 h access pattern within the urban agglomeration, whereas three urban agglomerations in the middle and lower reaches of the city only reached the 2 h access level in the center. Second, the Yellow River Basin was divided into six community spaces using the SLPA model based on the economic linkage between the central city and other cities, which were filtered by the 3 h access time from the central city to each city for HSR travel. Three of the six communities produced overlapping spaces, i.e., Community 3 and Community 4 produced overlapping spaces containing Linfen, Community 3 and Community 5 produced overlapping spaces containing Changzhi, Handan, and Xingtai, and Community 4 and Community 5 produced overlapping spaces containing Yuncheng and Sanmenxia. Third, the overlapping space of Changzhi City was selected as a case study for a visionary strategic planning outlook. Combining the geographic location characteristics and future development opportunities of Changzhi, we can try to transform a pass-through node like Changzhi into a hub node in the future, strengthening the gateway status and expanding the hinterland. According to the results of the research and analysis, policymakers can try to implement the expansion and renovation of HSR trunk lines, break the transportation bottlenecks in less developed areas, improve the coverage of the HSR network, and establish a “cross-urban agglomeration” cooperation and coordination mechanism. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

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33 pages, 3440 KiB

Open AccessReview

Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation

by Yue Ying, Mila Koeva, Monika Kuffer and Jaap Zevenbergen

ISPRS Int. J. Geo-Inf. 2023, 12(1), 2; https://doi.org/10.3390/ijgi12010002 - 22 Dec 2022

Cited by 4 | Viewed by 4237

Abstract

Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies [...] Read more.

Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general context of digital twin (DT) creation are not sufficiently explored. Therefore, this review aims to identify appropriate urban 3D modelling method(s) for city DT, which can be used for 3D property valuation (3DPV) in the future (both short-term and long-term). We focused on 3D modelling studies investigating buildings and urban elements directly linked with residential properties. In total, 180 peer-reviewed journal papers were selected between 2016 and 2020 with a narrative review approach. Analytical criteria for 3D modelling methods were explicitly defined and covered four aspects: metadata, technical characteristics, users’ requirements, and ethical considerations. From this, we derived short-term and long-term prospects for 3DPV. The results provide references for integrating 3D modelling and DT in property valuation and call for interdisciplinary collaboration including researchers and stakeholders in the real estate sector, such as real estate companies, house buyers and local governments. Full article

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17 pages, 4485 KiB

Open AccessArticle

Directional and Weighted Urban Network Analysis in the Chengdu-Chongqing Economic Circle from the Perspective of New Media Information Flow

by Changwei Xiao, Chunxia Liu and Yuechen Li

ISPRS Int. J. Geo-Inf. 2023, 12(1), 1; https://doi.org/10.3390/ijgi12010001 - 20 Dec 2022

Cited by 4 | Viewed by 1734

Abstract

The study of the two-way information flow between cities is of great significance to promote regional coordinated development, but the current mainstream non-directional network analysis method cannot analyze it effectively. In this paper, the quantities of relevant media articles in WeChat and Weibo [...] Read more.

The study of the two-way information flow between cities is of great significance to promote regional coordinated development, but the current mainstream non-directional network analysis method cannot analyze it effectively. In this paper, the quantities of relevant media articles in WeChat and Weibo between cities are taken as the traffic indices to construct a directional and weighted urban network of the Chengdu-Chongqing Economic Circle in China. Based on this network construction method, which adds direction thinking, we analyze the characteristics of information interconnection between cities. According to the analysis, we find that the provincial boundary hinders information interconnection, and the imbalance of external information interconnection is more serious in Chongqing’s central urban area, Liangping, Ya’an and Mianyang. In addition, we analyze the centrality status of different cities in the outward and inward perspective and further explore the factors that cause these differences in centrality. The results show that the centrality of the information network is not sensitive to the basic strength of the city, and it is the accessibility, including high-speed rail transportation access and telecommunication access, which controls the centrality of the city network. Full article

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ISPRS Int. J. Geo-Inf., Volume 12, Issue 1 (January 2023) – 25 articles

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