Special Issue "Geographic Complexity: Concepts, Theories, and Practices"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Prof. Dr. Changxiu Cheng
E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: geographic complexity; complexity theory and science; geographic information sciences (GIS); remote sensing (RS) image processing; analysis of geographical big data; spatial database; GIS theory and practice; high-performance computing; land ecology
Dr. Peichao Gao
E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: geographic complexity; complexity theory and science; geographic information sciences (GIS); remote sensing (RS) image processing; transportation; parallel/cloud computing; ecological informatics; Boltzmann/Shannon entropy; fractals
Prof. Dr. Samuel A. Cushman
E-Mail Website
Guest Editor
Rocky Mountain Research Station, USDA Forest Service, 2500 S. Pine Knoll Dr., Flagstaff, AZ 86001, USA
Interests: landscape ecology; landscape genetics; forest ecology; climate change; wildlife ecology; disturbance ecology; population biology; landscape dynamic simulation modeling; landscape pattern analysis
Special Issues and Collections in MDPI journals
Dr. Hung Chak Ho
E-Mail Website
Guest Editor
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
Interests: disabled-friendly environment; urban health and environmental epidemiology; social deprivation; applied demography; livelihood vulnerability; geospatial modeling

Special Issue Information

Dear Colleagues,

Our geography is a fundamentally important discipline with a long history. Focused on the surface of the Earth, research in this discipline has been conducted according to four dominant paradigms. The first is empirical, namely by describing geographic phenomena. The second is theoretical, in which some models and general laws are employed and tested. The third is computational, where geographic phenomena are simulated using digital computers with small real-world data sets. The last and most recent paradigm is eScience or data-intensive inquiries with big geographic data.

Since the Earth is a complex system, geographic research can benefit from theories and methods from complexity science (or sometimes complexity theory), which is the study of complex systems as “macroscopic collections of many basic but interacting units that are endowed with the potential to evolve in time” (O'Sullivan et al. 2006, p. 612). However, the use of complexity science in geographic research is somewhat limited, although several calls have been made and “geographic complexity” has been discussed (e.g., Manson 2007; Cushman 2016; Gao et al. 2017; Cheng et al. 2018; Shen et al. 2018; Song et al. 2018; Zhang et al. 2018).

This Special Issue aspires to further advance the frontiers of geographic complexity. The Special Issue’s guest editors invite submissions of original research from the communities related to the concepts, theories, or practices of geographic complexity. Topics include but are not limited to:

  • Complexity science and geography;
  • Entropic measures (e.g., Boltzmann entropy, Shannon entropy, mutual information, or joint entropy) for spatial data;
  • Fractals for geographic data;
  • Concepts, theories, or practices of geographic complexity;
  • Visualization and/or analysis of complex geographic data;
  • Nonlinearity, emergence, spontaneous order, adaptation, and/or feedback loops of geographic/spatial data;
  • Nonlinear and dynamic models in geography.

Cheng CX, Shi PJ, Song CQ, Gao JB (2018) Geographic big-data: A new opportunity for geography complexity study. Acta Geographica Sinica 73(8):1397–1406.

Cushman SA (2016) Calculating the configurational entropy of a landscape mosaic. Landscape Ecology 31(3):481–489.

Gao PC, Zhang H, Li ZL (2017) A hierarchy-based solution to calculate the configurational entropy of landscape gradients. Landscape Ecology 32(6):1133–1146.

Manson SM (2007) Challenges in evaluating models of geographic complexity. Environment and Planning B: Planning and Design 34(2):245–260.

O'Sullivan D, Manson SM, Messina JP, Crawford TW (2006) Space, place, and complexity science. Environment and Planning A 38:611–617.

Shen S, Ye SJ, Cheng CX et al (2018) Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China. Journal of Geophysical Research: Atmospheres 123(17):8936–8948.

Song CQ, Cheng CX, Shi PJ (2018) Geography complexity: New connotations of geography in the new era. Acta Geographica Sinica 73(7):1204–1213.

Zhang T, Shen S, Cheng CX, Song CQ, Ye SJ (2018) Long-range correlation analysis of soil temperature and moisture on A'rou Hillsides, Babao River Basin. Journal of Geophysical Research: Atmospheres 123(22):12,606–12,620.

Prof. Dr. Changxiu Cheng
Dr. Peichao Gao
Prof. Dr. Samuel A. Cushman
Dr. Hung Chak Ho
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 1400 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 (13 papers)

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Article
Spatiotemporal Evolution and Determinant Factors of the Intra-Regional Trade Community Structures of the Indian Ocean Region
ISPRS Int. J. Geo-Inf. 2021, 10(4), 214; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040214 - 01 Apr 2021
Viewed by 392
Abstract
The Indian Ocean Region (IOR) has become one of the main economic forces globally, and countries within the IOR have attempted to promote their intra-regional trade. This study investigates the spatiotemporal evolution of the community structures of the intra-regional trade and the impact [...] Read more.
The Indian Ocean Region (IOR) has become one of the main economic forces globally, and countries within the IOR have attempted to promote their intra-regional trade. This study investigates the spatiotemporal evolution of the community structures of the intra-regional trade and the impact of determinant factors on the formation of trade community structures of the IOR from 1996 to 2017 using the methods of social network analysis. Trade communities are groups of countries with measurably denser intra-trade ties but with extra-trade ties that are measurably sparser among different communities. The results show that the extent of trade integration and the trade community structures of the IOR changed from strengthening between 1996 and 2014 to weakening between 2015 and 2017. The largest explanatory power of the formation of the IOR trade community structures was the IOR countries’ economic size, indicating that market remained the strongest driver. The second-largest explanatory power was geographical proximity, suggesting that countries within the IOR engaged in intra-regional trade still tended to select geographically proximate trading partners. The third- and the fourth-largest were common civilization and regional organizational memberships, respectively. This indicates that sharing a common civilization and constructing intra-regional institutional arrangements (especially open trade policies) helped the countries within the IOR strengthen their trade communities. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Study on Fractal Characteristics of Migration-Population Flow—Evidence from Egypt
ISPRS Int. J. Geo-Inf. 2021, 10(2), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020045 - 21 Jan 2021
Viewed by 597
Abstract
Population migration is a major event of optimizing the allocation of production factors and a key way to construct regional relations and promote spatial reconstruction. However, there are few papers published on population migration that have a direct impact on the sustainable development [...] Read more.
Population migration is a major event of optimizing the allocation of production factors and a key way to construct regional relations and promote spatial reconstruction. However, there are few papers published on population migration that have a direct impact on the sustainable development of deserts owing to the more sensitive and complex man–earth relationship. Therefore, it is important to study the laws and characteristics of population migration in such regions. The study of Egypt by Zipf’s law shows that the spatial distribution of migration population size conforms to the law of power function with fractal characteristics of different types. The migration population is generally in a “Pareto” state in spatial distribution. Decentralization power is the leading driving force of spatial distribution, and scale-free regional distribution shows significant spatial agglomeration and gradient. Limited by research scope and “heavy tail” factors, Zipf’s law is not completely applicable. The spatial pattern and flow field characteristics of the migration population are analyzed in this paper by the conversion from “structural fractal” to “spatial fractal” based on scale-free geographic projection, providing the reference for the formulation of population governance policies and spatial planning strategies in Egypt and more desert countries. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets
ISPRS Int. J. Geo-Inf. 2020, 9(8), 483; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080483 - 11 Aug 2020
Viewed by 917
Abstract
Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and [...] Read more.
Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and their varied distribution, a consistent, larger-scale, and standardized framework for heterogeneity information extraction from this complex perspective is still lacking. Consequently, we developed a new Land Cover Complexity Index (LCCI), which is based on information-theory. The LCCI contains two foundational aspects of heterogeneity, composition and configuration, thereby capturing more comprehensive information on land cover patterns than any single metric approach. In this study, we compare the performance of the LCCI with that of other landscape metrics at two different scales, and the results show that our newly developed indicator more accurately characterizes and distinguishes different land cover patterns. LCCI provides an alternative way to measure the spatial variation of land cover distribution. Classification maps of land cover heterogeneity generated using the LCCI provide valuable insights and implications for regional conservation planning. Thus, the LCCI is shown to be a consistent indicator for the quantification of land cover heterogeneity that functions in an adaptive way by simultaneously considering both composition and configuration. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Using Social Networks to Analyze the Spatiotemporal Patterns of the Rolling Stock Manufacturing Industry for Countries in the Belt and Road Initiative
ISPRS Int. J. Geo-Inf. 2020, 9(7), 431; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070431 - 09 Jul 2020
Cited by 2 | Viewed by 865
Abstract
The new wave of modern rail transit and the proposal of the Belt and Road Initiative (BRI) have complicated the business patterns of the rolling stock manufacturing industry (RSMI) and the export of rolling stock products, especially in the case of countries participating [...] Read more.
The new wave of modern rail transit and the proposal of the Belt and Road Initiative (BRI) have complicated the business patterns of the rolling stock manufacturing industry (RSMI) and the export of rolling stock products, especially in the case of countries participating in the BRI. Based on the analysis of trade patterns—which focuses on the evolution of trade links, community structures, and intraregional export competitiveness—this study aims to explore the changes in the RSMI within the BRI region from 2003 to 2017. Sequential clustering was applied to the creation of a three-phase timeline. The network models of the cumulative trade of the rolling stock products and trades of two typical categories of products were constructed in each phase for the evolution analysis. Social network analysis methods, such as the analysis of network indices and community detection, were also applied. The results show that from 2003 to 2017, the connectivity of the rolling stock trade in this region significantly increased. China was the largest exporter, with increasing trade influence and technological strength. Ukraine and Russia were less competitive and highly mutually dependent. Czechia and Austria’s competitiveness remained prominent, but compared with China they lacked expansive vitality. South Korea was also an active and competitive country with strong technological prowess. These countries accounted for the majority of the exports, and were always at the center of their own separate communities, over which they maintained a sphere of influence. The grouping of countries far from any such spheres of influence changed frequently. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Experiment in Finding Look-Alike European Cities Using Urban Atlas Data
ISPRS Int. J. Geo-Inf. 2020, 9(6), 406; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060406 - 26 Jun 2020
Viewed by 916
Abstract
The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents [...] Read more.
The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II
ISPRS Int. J. Geo-Inf. 2020, 9(4), 236; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040236 - 10 Apr 2020
Viewed by 709
Abstract
Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed [...] Read more.
Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed as an improved parallelized multi-objective optimization method for complex geographical spatial sampling. Based on the AMOSA method, multiple Markov chains are used to extend the traditional single Markov chain; multi-core parallelization technology is employed based on multi-Markov chains. The tabu-archive constraint is designed to avoid repeated searches for optimal solutions. Two cases were investigated: one with six typical traditional test problems, and the other for soil spatial sampling optimization applications. Six performance indices of the two cases were analyzed—computational time, convergence, purity, spacing, min-spacing and displacement. The results revealed that AMOSA-II performed better which was more effective in obtaining preferable optimal solutions compared with AMOSA and NSGA-II. AMOSA-II can be treated as a feasible means to apply in other complex geographical spatial sampling optimizations. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities
ISPRS Int. J. Geo-Inf. 2020, 9(4), 192; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040192 - 25 Mar 2020
Cited by 2 | Viewed by 1147
Abstract
Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, [...] Read more.
Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, but previous studies have seldom characterized the heavy-tailed distribution of street connectivities from a fractal perspective. The long-tail distribution of street connectivities can be fractal under the new, third definition: a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The number of recurred scaling patterns of far more less-connected streets than well-connected ones greatly helps in measuring the scaling hierarchy of a street network. Moreover, it enables us to examine the potential fractality of urban street networks at the national scale. In this connection, the present study aims to contribute to urban morphology in China through the investigation of the ubiquity of fractal cities from the lens of street networks. To do this, we generate hundreds of thousands of natural streets from about 4.5 million street segments over 298 Chinese cities and adopted power-law detection as well as three fractal metrics that emerged from the third definition of fractal. The results show that almost all cities bear a fractal structure in terms of street connectivities. Furthermore, our multiple regression analysis suggests that the fractality of street networks is positively correlated with urban socioeconomic status and negatively correlated with energy consumption. Therefore, the fractal metrics can be a useful supplement to traditional street-network configuration measures such as street lengths. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
A Head/Tail Breaks-Based Method for Efficiently Estimating the Absolute Boltzmann Entropy of Numerical Raster Data
ISPRS Int. J. Geo-Inf. 2020, 9(2), 103; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020103 - 07 Feb 2020
Cited by 2 | Viewed by 915
Abstract
Shannon entropy is the most popular method for quantifying information in a system. However, Shannon entropy is considered incapable of quantifying spatial data, such as raster data, hence it has not been applied to such datasets. Recently, a method for calculating the Boltzmann [...] Read more.
Shannon entropy is the most popular method for quantifying information in a system. However, Shannon entropy is considered incapable of quantifying spatial data, such as raster data, hence it has not been applied to such datasets. Recently, a method for calculating the Boltzmann entropy of numerical raster data was proposed, but it is not efficient as it involves a series of numerical processes. We aimed to improve the computational efficiency of this method by borrowing the idea of head and tail breaks. This paper relaxed the condition of head and tail breaks and classified data with a heavy-tailed distribution. The average of the data values in a given class was regarded as its representative value, and this was substituted into a linear function to obtain the full expression of the relationship between classification level and Boltzmann entropy. The function was used to estimate the absolute Boltzmann entropy of the data. Our experimental results show that the proposed method is both practical and efficient; computation time was reduced to about 1% of the original method when dealing with eight 600 × 600 pixel digital elevation models. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Landscape Sustainability Evaluation of Ecologically Fragile Areas Based on Boltzmann Entropy
ISPRS Int. J. Geo-Inf. 2020, 9(2), 77; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020077 - 29 Jan 2020
Cited by 4 | Viewed by 803
Abstract
From the perspective of landscape, it is important to evaluate the landscape sustainability of ecologically fragile areas and explore temporal and spatial evolution laws to promote their sustainable development. Presently, most studies on the analysis of landscape Boltzmann entropy (also called configurational entropy) [...] Read more.
From the perspective of landscape, it is important to evaluate the landscape sustainability of ecologically fragile areas and explore temporal and spatial evolution laws to promote their sustainable development. Presently, most studies on the analysis of landscape Boltzmann entropy (also called configurational entropy) are based on a single landscape, and most of these studies are theoretical discussions. However, there are few case studies on landscape ecology. The main objectives of this paper are to explore a quantitative relationship between Boltzmann entropy and landscape sustainability, to propose a method for evaluating landscape sustainability based on Boltzmann entropy, and to evaluate the sustainability of diverse landscapes in Mizhi County, Shaanxi Province, China. This article uses digital elevation model (DEM) data with a spatial resolution of 30 m in Mizhi County. The remote sensing data on Mizhi County from 2000 were obtained by the Landsat Enhanced Thematic Mapper (ETM) + sensor, and the high-resolution image of Mizhi County from 2015 was obtained by the Gaofen-1 satellite. In this article, the subbasins are taken as the evaluation unit, and the Boltzmann entropy of Mizhi County is calculated based on the experts’ scoring of landscape sustainability in the study area. Through the analysis of landscape sustainability results from 216 subbasins in Mizhi County in 2000 and 2015, the following conclusions are drawn: (1) the evaluation matrix proposed in this paper is effective, and the Boltzmann entropy obtained by this method can directly reflect the level of landscape sustainability; (2) during the research period, the landscape sustainability of Mizhi County showed a good trend overall, especially the three townships of Taozhen, Shadian, and Shigou, which were significantly improved, and these findings were consistent with the field investigation; (3) on the spatial level, the landscape sustainability of mid-eastern Mizhi County is relatively poor compared to that in other regions, but the sustainability is also slowly increasing. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Clustering Complex Trajectories Based on Topologic Similarity and Spatial Proximity: A Case Study of the Mesoscale Ocean Eddies in the South China Sea
ISPRS Int. J. Geo-Inf. 2019, 8(12), 574; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120574 - 11 Dec 2019
Cited by 3 | Viewed by 835
Abstract
Many real-world dynamic features such as ocean eddies, rain clouds, and air masses may split or merge while they are migrating within a space. Topologically, the migration trajectories of such features are structurally more complex as they may have multiple branches due to [...] Read more.
Many real-world dynamic features such as ocean eddies, rain clouds, and air masses may split or merge while they are migrating within a space. Topologically, the migration trajectories of such features are structurally more complex as they may have multiple branches due to the splitting and merging processes. Identifying the spatial aggregation patterns of the trajectories could help us better understand how such features evolve. We propose a method, a Global Similarity Measuring Algorithm for the Complex Trajectories (GSMCT), to examine the spatial proximity and topologic similarity among complex trajectories. The method first transforms the complex trajectories into graph structures with nodes and edges. The global similarity between two graph structures (i.e., two complex trajectories) is calculated by averaging their topologic similarity and the spatial proximity, which are calculated using the Comprehensive Structure Matching (CSM) and the Hausdorff distance (HD) methods, respectively. We applied the GSMCT, the HD, and the Dynamic Time Warping (DTW) methods to examine the complex trajectories of the 1993–2016 mesoscale eddies in the South China Sea (SCS). Based on the similarity evaluation results, we categorized the complex trajectories across the SCS into four groups, which are similar to the zoning results reported in previous studies, though difference exists. Moreover, the yearly numbers of complex trajectories in the clusters in the northernmost (Cluster 1) and the southernmost SCS (Cluster 4) are almost the same. However, their seasonal variation and migration characteristics are totally opposite. Such new knowledge is very useful for oceanographers of interest to study and numerically simulate the mesoscale ocean eddies in the SCS. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data
ISPRS Int. J. Geo-Inf. 2019, 8(8), 358; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8080358 - 13 Aug 2019
Cited by 4 | Viewed by 1515
Abstract
Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment [...] Read more.
Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Article
Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System
ISPRS Int. J. Geo-Inf. 2019, 8(6), 281; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060281 - 16 Jun 2019
Viewed by 1616
Abstract
Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city [...] Read more.
Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Technical Note
PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy
ISPRS Int. J. Geo-Inf. 2020, 9(10), 594; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100594 - 10 Oct 2020
Viewed by 879
Abstract
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. [...] Read more.
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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