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

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 86001, AZ, 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 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 (5 papers)

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Research

Open AccessArticle
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://doi.org/10.3390/ijgi9020103 - 07 Feb 2020
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)
Open AccessArticle
Landscape Sustainability Evaluation of Ecologically Fragile Areas Based on Boltzmann Entropy
ISPRS Int. J. Geo-Inf. 2020, 9(2), 77; https://doi.org/10.3390/ijgi9020077 - 29 Jan 2020
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 rela Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Open AccessArticle
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://doi.org/10.3390/ijgi8120574 - 11 Dec 2019
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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Open AccessArticle
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://doi.org/10.3390/ijgi8080358 - 13 Aug 2019
Cited by 1
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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Open AccessArticle
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://doi.org/10.3390/ijgi8060281 - 16 Jun 2019
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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