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New Geospatial Science: Analytics and Management for Large Geospatial Datasets

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 42491

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Special Issue Editors

Dr. Nicholas Hamm

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Guest Editor

Geospatial and Geohazards Research Group, Faculty of Science and Engineering, University of Nottingham, Ningbo 315100, China
Interests: geospatial uncertainty; spatial data quality; geostatistics; spatial–temporal analysis; spatial prediction; environmental data analysis

Dr. Qian (Chayn) Sun

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Guest Editor

Geospatial Science, School of Science, RMIT University, Melbourne, Australia
Interests: spatial statistics and analysis; health geography; climate change adaptation; machine learning; urban spatial modelling; human perception and behaviour
Special Issues, Collections and Topics in MDPI journals

Dr. Keone Kelobonye

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Guest Editor

Department of Architecture and Planning, Faculty of Engineering and Technology, University of Botswana, Gaborone, Botswana
Interests: spatial–temporal analysis; spatial equity; transport and land use integration; accessibility; public transport; urban spatial structure

Special Issue Information

Dear Colleagues,

The last 20 years have seen an enormous increase in the volume and diversity of geospatial data. Alongside this, there have been advances in the analysis, modelling and management of geospatial data. Key areas of interest are:

Data and data integration/fusion. This includes current and historic remote sensing and environmental sensor networks as well as data from novel low-cost sensor networks, drones and process simulators. Spatially referenced socioeconomic data and other infrastructure data are also available. We also have volunteered geographic information (VGI) as well as data from social media, etc. All these data are increasingly available online. Key issues include accessibility, data quality, heterogeneity, scale and sampling. New approaches on big multidisciplinary spatial data integration and fusion are particularly welcome in this issue.
Analysis (or analytics), modelling and visualisation. This has traditionally focused on statistical methods but recently, machine learning, artificial intelligence and visualisation have gained increased interest. Key issues include (i) exploratory analysis and visualisation, (ii) the integration of large, diverse, heterogeneous and misaligned data and (iii) interpretation of the results. On this last point, meaningful interpretation of the results, identification of causal pathways and ethics are all important.
Data management and geocomputation. Large, diverse datasets present challenges for data management and for geocomputation. Open data is an important principle for geospatial data science. This extends beyond making data available towards making them usable by standards-based geocomputation tools and towards visualising results.
Uncertainty, scale and data quality. This is relevant to all of the above three issues. What is the quality of the data? How can we incorporate uncertainty into analysis, modelling and visualisation? How can uncertainty be managed and presented to users? Fundamental and theoretical papers related to above issues are particularly encouraged for submission.
Applications on COVID-19 original research. Case studies and applications related to COVID-19 using big geospatial data are welcome.

In this Special Issue, we welcome papers that address any the above issues. Although we welcome papers that address any application, we give particular emphasis on urban applications. Possibilities include: urban land use mapping and planning, smart cities, transportation, environmental pollution, urban ecology, urban environments and health.

Dr. Nicholas Hamm
Dr Qian (Chayn) Sun
Dr. Keone Kelobonye
Guest Editor

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

novel geospatial datasets
geospatial data integration
spatial data quality
geospatial uncertainty
geospatial analytics and visualisation
urban informatics
urban environment
urban health
COVID-19

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Published Papers (9 papers)

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Research

21 pages, 23338 KiB

Open AccessArticle

Context-Aware Matrix Factorization for the Identification of Urban Functional Regions with POI and Taxi OD Data

by Changfeng Jing, Yanru Hu, Hongyang Zhang, Mingyi Du, Shishuo Xu, Xian Guo and Jie Jiang

ISPRS Int. J. Geo-Inf. 2022, 11(6), 351; https://doi.org/10.3390/ijgi11060351 - 16 Jun 2022

Cited by 6 | Viewed by 2491

Abstract

The identification of urban functional regions (UFRs) is important for urban planning and sustainable development. Because this involves a set of interrelated processes, it is difficult to identify UFRs using only single data sources. Data fusion methods have the potential to improve the identification accuracy. However, the use of existing fusion methods remains challenging when mining shared semantic information among multiple data sources. In order to address this issue, we propose a context-coupling matrix factorization (CCMF) method which considers contextual relationships. This method was designed based on the fact that the contextual relationships embedded in all of the data are shared and complementary to one another. An empirical study was carried out by fusing point-of-interest (POI) data and taxi origin–destination (OD) data in Beijing, China. There are three steps in CCMF. First, contextual information is extracted from POI and taxi OD trajectory data. Second, fusion is performed using contextual information. Finally, spectral clustering is used to identify the functional regions. The results show that the proposed method achieved an overall accuracy (OA) of 90% and a kappa of 0.88 in the study area. The results were compared with the results obtained using single sources of non-fused data and other fusion methods in order to validate the effectiveness of our method. The results demonstrate that an improvement in the OA of about 5% in comparison to a similar method in the literature could be achieved using this method. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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28 pages, 9249 KiB

Open AccessArticle

Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

by Salwa Rizqina Putri, Arie Wahyu Wijayanto and Anjar Dimara Sakti

ISPRS Int. J. Geo-Inf. 2022, 11(5), 275; https://doi.org/10.3390/ijgi11050275 - 24 Apr 2022

Cited by 21 | Viewed by 7395

Abstract

Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO₂), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value =

5.97 \times 10^{- 7}

) and Spearman rank correlation coefficient = 0.77 (p-value =

1.58 \times 10^{- 8}

) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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16 pages, 8228 KiB

Open AccessArticle

Precise Indoor Path Planning Based on Hybrid Model of GeoSOT and BIM

by Huangchuang Zhang and Ge Li

ISPRS Int. J. Geo-Inf. 2022, 11(4), 243; https://doi.org/10.3390/ijgi11040243 - 8 Apr 2022

Cited by 4 | Viewed by 2635

Abstract

With the improvement of urban infrastructure and the increase in the coverage of high-rise buildings, the demand for location information services inside buildings is becoming more and more urgent. Moreover, indoor path planning, as a prerequisite and basis for realizing path guidance inside buildings, has become a research focus in the field of location services. This makes the accurate planning of indoor paths an urgent problem to be solved at present. This requires dynamic and precise planning from static fuzzy planning, and the corresponding scene converted from a two-dimensional plane to a three-dimensional one. However, most of the existing indoor path planning methods focus on the use of two-dimensional floor plans in buildings to build indoor maps and rely on traditional path search algorithms for pathfinding, which lack in the efficient use of the building’s own geometric and attribute information and lack consideration of the internal spatial topology of the building, making it difficult to meet the needs of indoor multi-layer continuous space path planning. Considering this relationship, it is difficult to meet the path planning needs of indoor multi-layer continuous spaces. In addition, the two-dimensional expression dominated by arrows and line drawings also greatly reduces the intuitiveness and interactivity of path expression. Regarding this, this paper combines the GeoSOT grid with accurate real geographic information and the BIM model and proposes an accurate indoor path planning method. Finally, using Guanlan Commercial Street in Baiyin City as the experimental object, the precise planning and generation of indoor paths and the interaction of visual displays on the web page are realized. It has been verified that the method has certain reference and application values for meeting the demand of location information services in buildings and building an integrated indoor–outdoor navigation service platform. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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19 pages, 7312 KiB

Open AccessArticle

Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

by Yiyi Huang, Tao Lin, Guoqin Zhang, Wei Zhu, Nicholas A. S. Hamm, Yuqin Liu, Junmao Zhang and Xia Yao

ISPRS Int. J. Geo-Inf. 2022, 11(4), 215; https://doi.org/10.3390/ijgi11040215 - 22 Mar 2022

Cited by 7 | Viewed by 3403

Abstract

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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22 pages, 11906 KiB

Open AccessArticle

Multidimensional Urban Vitality on Streets: Spatial Patterns and Influence Factor Identification Using Multisource Urban Data

by Qian Li, Caihui Cui, Feng Liu, Qirui Wu, Yadi Run and Zhigang Han

ISPRS Int. J. Geo-Inf. 2022, 11(1), 2; https://doi.org/10.3390/ijgi11010002 - 28 Dec 2021

Cited by 64 | Viewed by 8011

Abstract

Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city’s central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R²) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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27 pages, 4974 KiB

Open AccessArticle

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

by Lih Wei Yeow, Raymond Low, Yu Xiang Tan and Lynette Cheah

ISPRS Int. J. Geo-Inf. 2021, 10(11), 735; https://doi.org/10.3390/ijgi10110735 - 29 Oct 2021

Cited by 26 | Viewed by 6668

Abstract

Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low (<60 m). Thematic accuracy in names and place types varied. The move towards standardized validation metrics depends on factors such as data availability for intrinsic or extrinsic methods, varying levels of detail across POI datasets, the influence of matching procedures, and the intended application of POI data. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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21 pages, 827 KiB

Open AccessArticle

A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics

by Fernando H. O. Abreu, Amilcar Soares, Fernando V. Paulovich and Stan Matwin

ISPRS Int. J. Geo-Inf. 2021, 10(6), 412; https://doi.org/10.3390/ijgi10060412 - 15 Jun 2021

Cited by 15 | Viewed by 3598

Abstract

With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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17 pages, 3405 KiB

Open AccessEditor’s ChoiceArticle

School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API

by Anqi Liu, Keone Kelobonye, Zhenqi Zhou, Qiuxia Xu, Zhen Xu and Lingyun Han

ISPRS Int. J. Geo-Inf. 2020, 9(9), 520; https://doi.org/10.3390/ijgi9090520 - 31 Aug 2020

Cited by 6 | Viewed by 3989

Abstract

Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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24 pages, 6998 KiB

Open AccessEditor’s ChoiceArticle

Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

by Qing Zhu, Meite Chen, Bin Feng, Yan Zhou, Maosu Li, Zhaowen Xu, Yulin Ding, Mingwei Liu, Wei Wang and Xiao Xie

ISPRS Int. J. Geo-Inf. 2020, 9(9), 518; https://doi.org/10.3390/ijgi9090518 - 28 Aug 2020

Cited by 4 | Viewed by 2715

Abstract

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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