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Ding Ma
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518060, China

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Journal article
Published: 27 August 2021 in Remote Sensing
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Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.

ACS Style

Heng Luo; Biao He; Renzhong Guo; Weixi Wang; Xi Kuai; Bilu Xia; Yuan Wan; Ding Ma; Linfu Xie. Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images. Remote Sensing 2021, 13, 3414 .

AMA Style

Heng Luo, Biao He, Renzhong Guo, Weixi Wang, Xi Kuai, Bilu Xia, Yuan Wan, Ding Ma, Linfu Xie. Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images. Remote Sensing. 2021; 13 (17):3414.

Chicago/Turabian Style

Heng Luo; Biao He; Renzhong Guo; Weixi Wang; Xi Kuai; Bilu Xia; Yuan Wan; Ding Ma; Linfu Xie. 2021. "Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images." Remote Sensing 13, no. 17: 3414.

Journal article
Published: 30 March 2021 in Remote Sensing
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A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.

ACS Style

Ding Ma; Renzhong Guo; Ying Jing; Ye Zheng; Zhigang Zhao; Jiahao Yang. Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery. Remote Sensing 2021, 13, 1322 .

AMA Style

Ding Ma, Renzhong Guo, Ying Jing, Ye Zheng, Zhigang Zhao, Jiahao Yang. Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery. Remote Sensing. 2021; 13 (7):1322.

Chicago/Turabian Style

Ding Ma; Renzhong Guo; Ying Jing; Ye Zheng; Zhigang Zhao; Jiahao Yang. 2021. "Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery." Remote Sensing 13, no. 7: 1322.

Journal article
Published: 08 February 2021 in Land
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Many cities face health issues that result from ineffective urban planning strategies. The chances of doing exercises in sportive venues implicate public health and citizen quality of life. With the advent of the geo-big data era, it is crucial to explore the spatial pattern of sports facilities to reflect urban health issues. This study aims to decode the street-based spatiality of gyms (one prevailing type of sportive venues) from a comprehensive perspective by both geometric methods (i.e., segment streets) and topological analytics in the context of complexity science (i.e., complex network derived from the topology of natural streets). We found that: (1) gyms are spatially clustered and distributed unevenly; (2) community-to-gym walkability fits the power-law with a heavy-tailed distribution at the 10-min and 20-min temporal scales; (3) the model for the street connectivity and the multi-distance reachability of gyms is with high polynomial fitting goodness. This article is conducive to strategies-making of healthy city planning and the further optimization of urban spatial structure.

ACS Style

Ying Jing; Ding Ma; Yaolin Liu; Jiaxing Cui; Sheng Zhang; Yiyun Chen. Decoding the Street-Based Spatiality of Urban Gyms: Implications for Healthy City Planning. Land 2021, 10, 175 .

AMA Style

Ying Jing, Ding Ma, Yaolin Liu, Jiaxing Cui, Sheng Zhang, Yiyun Chen. Decoding the Street-Based Spatiality of Urban Gyms: Implications for Healthy City Planning. Land. 2021; 10 (2):175.

Chicago/Turabian Style

Ying Jing; Ding Ma; Yaolin Liu; Jiaxing Cui; Sheng Zhang; Yiyun Chen. 2021. "Decoding the Street-Based Spatiality of Urban Gyms: Implications for Healthy City Planning." Land 10, no. 2: 175.

Journal article
Published: 23 November 2020 in Sustainability
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Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

ACS Style

Wei Zhu; Ding Ma; Zhigang Zhao; Renzhong Guo. Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data. Sustainability 2020, 12, 9778 .

AMA Style

Wei Zhu, Ding Ma, Zhigang Zhao, Renzhong Guo. Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data. Sustainability. 2020; 12 (22):9778.

Chicago/Turabian Style

Wei Zhu; Ding Ma; Zhigang Zhao; Renzhong Guo. 2020. "Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data." Sustainability 12, no. 22: 9778.

Journal article
Published: 10 November 2020 in IEEE Access
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Urban arterial traffic coordination control has attracted much attention in smart city construction process. To achieve optimal signal timing, many studies have attempted to adjust green splits of a cycle time according to the distance between road intersections. However, existing green wave traffic control systems usually require a sophisticated calculation that depend upon the stability of vehicle speed and traffic flow, which can lead to weak robustness. Therefore, this paper proposes two novel approaches to control arterial traffic coordination with the help of artificial intelligence: DDPG-BAND and ES-BAND. DDPG-BAND has two stages: a coarse-tuning stage reduces the blocking coefficient, and a fine-tuning stage optimizes the traffic evaluation index. ES-BAND introduces the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), a scalable alternative to reinforcement learning, into signal timing. Different traffic variables are adopted as parameters to search for the optimal value by the CMA-ES. To evaluate the feasibility and effectiveness of our approaches, we import real traffic flow data from Zhongshan Road, Ningbo, Zhejiang Province, China, into a traffic environment simulator for training and then conduct a series of experiments. The results show that ES-BAND outperforms the traditional methods in terms of better convergence, lower journey time, fewer stops, and more throughput.

ACS Style

Ye Zheng; Renzhong Guo; Ding Ma; Zhigang Zhao; XiaoMing Li. A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy. IEEE Access 2020, 8, 214115 -214127.

AMA Style

Ye Zheng, Renzhong Guo, Ding Ma, Zhigang Zhao, XiaoMing Li. A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy. IEEE Access. 2020; 8 (99):214115-214127.

Chicago/Turabian Style

Ye Zheng; Renzhong Guo; Ding Ma; Zhigang Zhao; XiaoMing Li. 2020. "A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy." IEEE Access 8, no. 99: 214115-214127.

Technical note
Published: 10 October 2020 in ISPRS International Journal of Geo-Information
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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.

ACS Style

Ding Ma; Zhigang Zhao; Ye Zheng; Renzhong Guo; Wei Zhu. PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy. ISPRS International Journal of Geo-Information 2020, 9, 594 .

AMA Style

Ding Ma, Zhigang Zhao, Ye Zheng, Renzhong Guo, Wei Zhu. PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy. ISPRS International Journal of Geo-Information. 2020; 9 (10):594.

Chicago/Turabian Style

Ding Ma; Zhigang Zhao; Ye Zheng; Renzhong Guo; Wei Zhu. 2020. "PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy." ISPRS International Journal of Geo-Information 9, no. 10: 594.

Journal article
Published: 25 March 2020 in ISPRS International Journal of Geo-Information
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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.

ACS Style

Ding Ma; Renzhong Guo; Ye Zheng; Zhigang Zhao; Fangning He; Wei Zhu. Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities. ISPRS International Journal of Geo-Information 2020, 9, 192 .

AMA Style

Ding Ma, Renzhong Guo, Ye Zheng, Zhigang Zhao, Fangning He, Wei Zhu. Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities. ISPRS International Journal of Geo-Information. 2020; 9 (4):192.

Chicago/Turabian Style

Ding Ma; Renzhong Guo; Ye Zheng; Zhigang Zhao; Fangning He; Wei Zhu. 2020. "Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities." ISPRS International Journal of Geo-Information 9, no. 4: 192.

Research article
Published: 24 January 2020 in International Journal of Geographical Information Science
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The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space.

ACS Style

Ding Ma; Toshihiro Osaragi; Takuya Oki; Bin Jiang. Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science 2020, 34, 2475 -2496.

AMA Style

Ding Ma, Toshihiro Osaragi, Takuya Oki, Bin Jiang. Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science. 2020; 34 (12):2475-2496.

Chicago/Turabian Style

Ding Ma; Toshihiro Osaragi; Takuya Oki; Bin Jiang. 2020. "Exploring the heterogeneity of human urban movements using geo-tagged tweets." International Journal of Geographical Information Science 34, no. 12: 2475-2496.

Journal article
Published: 01 September 2018 in Cartographica: The International Journal for Geographic Information and Geovisualization
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It is commonly believed in the literature that smooth curves, such as circles, are not fractal, and only non-smooth curves, such as coastlines, are fractal. However, this article demonstrates that a smooth curve can be fractal, under a new, relaxed, third definition of fractal – a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The scaling can be rephrased as a hierarchy, consisting of numerous smallest, a very few largest, and some in between the smallest and the largest. The logarithmic spiral, as a smooth curve, is apparently fractal because it bears the self-similarity property, or the scaling of far more small squares than large ones recurs multiple times, or the scaling of far more small bends than large ones recurs multiple times. A half-circle or half-ellipse and the UK coastline (before or after smooth processing) are fractal if the scaling of far more small bends than large ones recurs at least twice.

ACS Style

Ding Ma; Bin Jiang. A Smooth Curve as a Fractal under the Third Definition. Cartographica: The International Journal for Geographic Information and Geovisualization 2018, 53, 203 -210.

AMA Style

Ding Ma, Bin Jiang. A Smooth Curve as a Fractal under the Third Definition. Cartographica: The International Journal for Geographic Information and Geovisualization. 2018; 53 (3):203-210.

Chicago/Turabian Style

Ding Ma; Bin Jiang. 2018. "A Smooth Curve as a Fractal under the Third Definition." Cartographica: The International Journal for Geographic Information and Geovisualization 53, no. 3: 203-210.

Preprint
Published: 26 February 2017
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A fractal bears a complex structure that is reflected in a scaling hierarchy of far more small things than large ones. This scaling hierarchy can be effectively derived by head/tail breaks - a classification scheme for data with a heavy-tailed distribution, and be quantified by ht-index - a head/tail breaks induced integer. This paper refines the ht-index as a fraction with which to measure the scaling hierarchy of a fractal more precisely within a whole, and further assigns a fractional ht-index to an individual data value of a data series that represents the fractal. The fractional ht-index or fractional hierarchy in general adds deep implications on creation of fractals or living structures. Keywords: Ht-index, fractal, scaling, complexity, fht-index

ACS Style

Bin Jiang; Ding Ma. How Complex Is a Fractal? Head/tail Breaks and Fractional Hierarchy. 2017, 1 .

AMA Style

Bin Jiang, Ding Ma. How Complex Is a Fractal? Head/tail Breaks and Fractional Hierarchy. . 2017; ():1.

Chicago/Turabian Style

Bin Jiang; Ding Ma. 2017. "How Complex Is a Fractal? Head/tail Breaks and Fractional Hierarchy." , no. : 1.

Article
Published: 18 February 2016 in Geographical Analysis
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Social media outlets such as Twitter constitute valuable data sources for understanding human activities in the virtual world from a geographic perspective. This article examines spatial distribution of tweets and densities within cities. The cities refer to natural cities that are automatically aggregated from a country's small street blocks, so called city blocks. We adopted street blocks (rather than census tracts) as the basic geographic units and topological center (rather than geometric center) to assess how tweets and densities vary from the center to the peripheral border. We found that, within a city from the center to the periphery, the tweets first increase and then decrease, while the densities decrease in general. These increases and decreases fluctuate dramatically, and differ significantly from those if census tracts are used as the basic geographic units. We also found that the decrease of densities from the center to the periphery is less significant, and even disappears, if an arbitrarily defined city border is adopted. These findings prove that natural cities and their topological centers are better than their counterparts (conventionally defined cities and city centers) for geographic research. Based on this study, we believe that tweet densities can be a good surrogate of population densities. If this belief is proved to be true, social media data could help solve the dispute surrounding exponential or power function of urban population density.

ACS Style

Bin Jiang; Ding Ma; Junjun Yin; Mats Sandberg. Spatial Distribution of City Tweets and Their Densities. Geographical Analysis 2016, 48, 337 -351.

AMA Style

Bin Jiang, Ding Ma, Junjun Yin, Mats Sandberg. Spatial Distribution of City Tweets and Their Densities. Geographical Analysis. 2016; 48 (3):337-351.

Chicago/Turabian Style

Bin Jiang; Ding Ma; Junjun Yin; Mats Sandberg. 2016. "Spatial Distribution of City Tweets and Their Densities." Geographical Analysis 48, no. 3: 337-351.

Journal article
Published: 01 June 2015 in Physica A: Statistical Mechanics and its Applications
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ACS Style

Bin Jiang; Ding Ma. Defining least community as a homogeneous group in complex networks. Physica A: Statistical Mechanics and its Applications 2015, 428, 154 -160.

AMA Style

Bin Jiang, Ding Ma. Defining least community as a homogeneous group in complex networks. Physica A: Statistical Mechanics and its Applications. 2015; 428 ():154-160.

Chicago/Turabian Style

Bin Jiang; Ding Ma. 2015. "Defining least community as a homogeneous group in complex networks." Physica A: Statistical Mechanics and its Applications 428, no. : 154-160.

Journal article
Published: 08 April 2015 in ISPRS International Journal of Geo-Information
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OpenStreetMap (OSM) constitutes an unprecedented, free, geographical information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavy-edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration.

ACS Style

Ding Ma; Mats Sandberg; Bin Jiang. Characterizing the Heterogeneity of the OpenStreetMap Data and Community. ISPRS International Journal of Geo-Information 2015, 4, 535 -550.

AMA Style

Ding Ma, Mats Sandberg, Bin Jiang. Characterizing the Heterogeneity of the OpenStreetMap Data and Community. ISPRS International Journal of Geo-Information. 2015; 4 (2):535-550.

Chicago/Turabian Style

Ding Ma; Mats Sandberg; Bin Jiang. 2015. "Characterizing the Heterogeneity of the OpenStreetMap Data and Community." ISPRS International Journal of Geo-Information 4, no. 2: 535-550.

Preprint
Published: 03 March 2015
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OpenStreetMap (OSM) constitutes an unprecedented, free, geographic information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements, and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power-law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavily edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration. Keywords: OpenStreetMap, big data, power laws, head/tail breaks, ht-index

ACS Style

Ding Ma; Mats Sandberg; Bin Jiang. Characterizing the Heterogeneity of the OpenStreetMap Data and Community. 2015, 1 .

AMA Style

Ding Ma, Mats Sandberg, Bin Jiang. Characterizing the Heterogeneity of the OpenStreetMap Data and Community. . 2015; ():1.

Chicago/Turabian Style

Ding Ma; Mats Sandberg; Bin Jiang. 2015. "Characterizing the Heterogeneity of the OpenStreetMap Data and Community." , no. : 1.