Special Issue "Intelligent Spatial Decision Support"

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

Deadline for manuscript submissions: closed (31 December 2016).

Special Issue Editors

Guest Editor
Prof. Dr. Shih-Lung Shaw Website E-Mail
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: transportation geography; geographic information science; time geography; human dynamics
Guest Editor
Prof. Dr. Qingquan Li Website E-Mail
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
Interests: geographic information science and precise engineering survey
Guest Editor
Assoc. Prof. Dr. Yang Yue Website E-Mail
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
Interests: spatial data modelling and data mining, GIS-T

Special Issue Information

Dear Colleagues,

Emerging technologies have instigated new challenges to existing theories, concepts, models and practices of spatial decision support. The increasing availability of big data at various spatial, temporal, and thematic scales, coupled with rapidly advancing information and communication technologies as well as location-aware technologies, offer opportunities to support more intelligent and timely spatial decisions. Traditional theories, models, methods, and data may be inadequate to cope with the complexity of real-life spatial decision problems today. It is therefore important to investigate innovative ways of taking full advantage of the latest technologies and data to develop intelligent spatial decision support solutions that can better address the complex spatial decision challenges we are facing today. This special issue aims at bringing together researchers in all related fields to discuss and stimulate innovative ideas of formulating research questions, designing research approaches, developing analytical methods, and supporting intelligent spatial decisions of real-world challenges.

This special issue will place an emphasis on the development of geospatial research frameworks, theories, methods and good case studies of tackling key research challenges related to intelligent spatial decision support. Sample topics include:

  • Theory, concepts, design and development of Spatial Planning Support Systems (SPSS) and Spatial Decision Support Systems (SDSS),
  • Theory, concepts, design and development of Integrated Planning and Decision Support Systems (IPDSS),
  • Design and development of Collaborative Spatial Decision Support Systems (CSDSS), considering various modelling techniques and requirements of different user-groups (style, functionality, etc.),
  • Theory, concepts and application of Spatial Multiple Criteria Decision Analysis (SMCDA) in single and group environment,
  • Multi-dimensional, multi-thematic and multi-resolution spatial information for spatial decision support systems,
  • Spatial decision modeling, collaborative and exploratory data analysis, and decision visualization,
  • GeoSocial networks, crowdsourcing and public participatory spatial decision support,
  • Data-intensive computing and computational intelligence for spatial decision support,
  • Web-based and cloud-based spatial decision support systems.

If you are not sure whether your potential contribution might fit the scope of this special issue, please get in touch with one of the guest editors.

Submission procedure:

Interested authors should notify the guest editors of their intention to submit a paper contribution by sending the title and a 250-word abstract to Yang Yue ([email protected]) by July 1, 2016. The deadline for submissions of the final papers is December 31, 2016.

A condition of submission and acceptance is that papers must pass the normal IJGI review process. For author instructions, please refer to “Instruction for Authors” (https://www.mdpi.com/journal/ijgi/instructions) at the IJGI journal homepage. All manuscripts, including support materials, must be submitted using the journal’s online MDPI Submission System site (https://susy.mdpi.com/user/manuscripts/upload?journal=ijgi). For questions, please contact Yang Yue ([email protected]).

Shih-Lung Shaw
Qingquan Li
Yang Yue
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be 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 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

Open AccessArticle
Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm
ISPRS Int. J. Geo-Inf. 2017, 6(5), 127; https://doi.org/10.3390/ijgi6050127 - 26 Apr 2017
Abstract
In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial [...] Read more.
In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic algorithm (IGA) and improved particle swarm optimization (PSO) to building displacement to solve conflicts. The dual-inheritance framework from the cultural algorithm is adopted in the PSO algorithm to optimize the topologic structure of particles. We generate Pareto optimal displacement solutions using the niche Pareto competition mechanism. The results of experiments comparing IGA and the improved PSO show that the improved PSO outperforms IGA; the improved PSO results in fewer graphic conflicts and smaller movements that better satisfy the movement precision requirements. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers
ISPRS Int. J. Geo-Inf. 2017, 6(2), 57; https://doi.org/10.3390/ijgi6020057 - 22 Feb 2017
Cited by 31
Abstract
Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible [...] Read more.
Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China
ISPRS Int. J. Geo-Inf. 2017, 6(2), 52; https://doi.org/10.3390/ijgi6020052 - 18 Feb 2017
Cited by 1
Abstract
With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from [...] Read more.
With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Collection and Integration of Local Knowledge and Experience through a Collective Spatial Analysis
ISPRS Int. J. Geo-Inf. 2017, 6(2), 33; https://doi.org/10.3390/ijgi6020033 - 24 Jan 2017
Cited by 1
Abstract
This article discusses the convenience of adopting an approach of Collective Spatial Analysis in the P/PGIS processes, with the aim of improving the collection and integration of knowledge and local expertise in decision-making, mainly in the fields of planning and adopting territorial policies. [...] Read more.
This article discusses the convenience of adopting an approach of Collective Spatial Analysis in the P/PGIS processes, with the aim of improving the collection and integration of knowledge and local expertise in decision-making, mainly in the fields of planning and adopting territorial policies. Based on empirical evidence, as a result of the review of scientific articles from the Web of Science database, in which it is displayed how the knowledge and experience of people involved in decision-making supported by P/PGIS are collected and used, a prototype of a WEB-GSDSS application has been developed. This prototype allows a group of people to participate anonymously, in an asynchronous and distributed way, in a decision-making process to locate goods, services, or events through the convergence of their views. Via this application, two case studies for planning services in districts of Ecuador and Italy were carried out. Early results suggest that in P/PGIS local and external actors contribute their knowledge and experience to generate information that afterwards is integrated and analysed in the decision-making process. On the other hand, in a Collective Spatial Analysis, these actors analyse and generate information in conjunction with their knowledge and experience during the process of decision-making. We conclude that, although the Collective Spatial Analysis approach presented is in a subjective and initial stage, it does drive improvements in the collection and integration of knowledge and local experience, foremost among them is an interdisciplinary geo-consensus. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach
ISPRS Int. J. Geo-Inf. 2016, 5(12), 240; https://doi.org/10.3390/ijgi5120240 - 13 Dec 2016
Cited by 7
Abstract
Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban [...] Read more.
Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban planning and management immensely. This study makes use of a large-scale mobile phone positioning dataset to characterize the diurnal dynamics of the interaction-based urban spatial structure. To extract the temporally vibrant structure, spatial interaction networks at different times are constructed based on the movement connections of individuals between geographical units. Complex network community detection technique is applied to identify the spatial divisions as well as to quantify their temporal dynamics. Empirical analysis is conducted using data containing all user positions on a typical weekday in Shenzhen, China. Results are compared with official zoning and planned structure and indicate a certain degree of expansion in urban central areas and fragmentation in industrial suburban areas. A high level of variability in spatial divisions at different times of day is detected with some distinct temporal features. Peak and pre-/post-peak hours witness the most prominent fluctuation in spatial division indicating significant change in the characteristics of movements and activities during these periods of time. Findings of this study demonstrate great potential of large-scale mobility data in supporting intelligent spatial decision making and providing valuable knowledge to the urban planning sectors. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Location Optimization Using a Hierarchical Location-Allocation Model for Trauma Centers in Shenzhen, China
ISPRS Int. J. Geo-Inf. 2016, 5(10), 190; https://doi.org/10.3390/ijgi5100190 - 11 Oct 2016
Cited by 5
Abstract
Trauma is considered a “modern civilized sickness”, and its occurrence substantially affects all of society, as well as individuals. The implementation of trauma emergency systems in cities with young, prosperous, and highly mobile populations is necessary and significant. A complete trauma emergency system [...] Read more.
Trauma is considered a “modern civilized sickness”, and its occurrence substantially affects all of society, as well as individuals. The implementation of trauma emergency systems in cities with young, prosperous, and highly mobile populations is necessary and significant. A complete trauma emergency system includes both low-level trauma centers that offer basic emergency services and high-level trauma centers that offer comprehensive services. GIS and operational research methods were used to solve the location problem associated with these centers. This study analyzed the spatial distribution characteristics of trauma demands and the candidate locations of trauma centers based on a spatial analysis and presented a hierarchical location-allocation model for low- and high-level trauma centers in Shenzhen. The response, coverage, treatment and cost capacities of the trauma center locations were considered, and an ant colony optimization was used to calculate the optimal solution. The objectives of this study were to optimize trauma center locations, improve the allocation of medical trauma resources and reduce the rate of deaths and disabilities due to trauma. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria
ISPRS Int. J. Geo-Inf. 2016, 5(10), 188; https://doi.org/10.3390/ijgi5100188 - 10 Oct 2016
Abstract
Open data sources regarding conflicts are increasingly enriched by broad social media; these yield a volume of information that exceeds our process capabilities. One of the critical factors is that knowledge extraction from mixed data formats requires systematic, sophisticated modeling. Here, we propose [...] Read more.
Open data sources regarding conflicts are increasingly enriched by broad social media; these yield a volume of information that exceeds our process capabilities. One of the critical factors is that knowledge extraction from mixed data formats requires systematic, sophisticated modeling. Here, we propose using text mining modeling tools for building associations of heterogeneous semi-structured data to enhance decision-making. Using narrative plots, text representation, and cluster analysis, we provide a data association framework that can mine spatiotemporal data that occur in similar contexts. The framework contains the following steps: (1) a novel text representation is presented to vectorize the textual semantics by learning both co-word features and word orders in a unified form; (2) text clustering technology is employed to associate events of interest with similar events in historical logs, based solely on narrative plots of the events; and (3) the inferred activity procedure is visualized via an evolving spatiotemporal map through the Kriging algorithm. Our results demonstrate that the approach enables deeper discrimination into the trends underlying conflicts and possesses a narrative reasoning forward prediction with a precision of 0.4817, in addition to a high consistency with the conclusions of existing studies. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Can Hawaii Meet Its Renewable Fuel Target? Case Study of Banagrass-Based Cellulosic Ethanol
ISPRS Int. J. Geo-Inf. 2016, 5(8), 146; https://doi.org/10.3390/ijgi5080146 - 16 Aug 2016
Cited by 1
Abstract
Banagrass is a biomass crop candidate for ethanol production in the State of Hawaii. This study examines: (i) whether enough banagrass can be produced to meet Hawaii’s renewable fuel target of 20% highway fuel demand produced with renewable sources by 2020 and (ii) [...] Read more.
Banagrass is a biomass crop candidate for ethanol production in the State of Hawaii. This study examines: (i) whether enough banagrass can be produced to meet Hawaii’s renewable fuel target of 20% highway fuel demand produced with renewable sources by 2020 and (ii) at what cost. This study proposes to locate suitable land areas for banagrass production and ethanol processing, focusing on the two largest islands in the state of Hawaii—Hawaii and Maui. The results suggest that the 20% target is not achievable by using all suitable land resources for banagrass production on both Hawaii and Maui. A total of about 74,224,160 gallons, accounting for 16.04% of the state’s highway fuel demand, can be potentially produced at a cost of $6.28/gallon. Lower ethanol cost is found when using a smaller production scale. The lowest cost of $3.31/gallon is found at a production processing capacity of about 9 million gallons per year (MGY), which meets about 2% of state demand. This cost is still higher than the average imported ethanol price of $3/gallon. Sensitivity analysis finds that it is possible to produce banagrass-based ethanol on Hawaii Island at a cost below the average imported ethanol price if banagrass yield increases of at least 35.56%. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach
ISPRS Int. J. Geo-Inf. 2016, 5(8), 131; https://doi.org/10.3390/ijgi5080131 - 26 Jul 2016
Cited by 13
Abstract
This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation [...] Read more.
This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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Open AccessArticle
A GIS- and Fuzzy Set-Based Online Land Price Evaluation Approach Supported by Intelligence-Aided Decision-Making
ISPRS Int. J. Geo-Inf. 2016, 5(7), 126; https://doi.org/10.3390/ijgi5070126 - 19 Jul 2016
Cited by 1
Abstract
In recent years, with the reforms to the land use system and the development of urbanization in China, land price evaluation has tended towards marketization. Prices are determined by the government, the land transaction market and the public. It is necessary to propose [...] Read more.
In recent years, with the reforms to the land use system and the development of urbanization in China, land price evaluation has tended towards marketization. Prices are determined by the government, the land transaction market and the public. It is necessary to propose higher standards to be used in the evaluation process. This paper presents an online land price evaluation approach for convenience in evaluation. In a network environment, taking advantage of the data services provided by various departments, we propose two models to assist in decision-making: (1) a geographic information system (GIS)- and fuzzy set-based location factor quantification model, which adopts dynamic data, rules and quantification measures (based on the road network) to dynamically quantify location factors, thus transforming fuzzy sets into appropriate values; and (2) a neartude-based transaction sample push model, which quantifies the similarity between a given land and other samples, thus providing a basis for decision-making by an appraiser. This approach is applied in Shenzhen to evaluate its ability to simplify the work of appraisers and make their decisions more intuitive and objective in a real case. Full article
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
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