Special Issue "Geospatial Big Data and Urban Studies"

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

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Prof. Dr. Maria Antonia Brovelli

GeoLab, Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Website | E-Mail
Interests: geoinformatics; free and open source GIS; geospatial web; geo big data; geo crowdsourcing
Guest Editor
Mr. Hussein M. Abdulmuttalib

Senior GIS Specialist, GIS Department of Dubai Municipality, UAE
Website | E-Mail
Interests: GI-Science; geo basemap data/big data; data quality; remote sensing; OBIA; spatial environmental monitoring; smart 3D surfaces and urban designs
Guest Editor
Dr. Peter Mooney

Department of Computer Science, Maynooth University, Maynooth, Ireland
Website | E-Mail
Interests: volunteered geographic information (VGI); citizen science; geospatial data mining and knowledge extraction; free and open source software for geomatics (FOSS4G)
Guest Editor
Prof. Chris Pettit

City Futures Research Centre, Built Environment UNSW, Sydney, Australia
Website | E-Mail
Interests: informed urbanization; big data; city dashboards; urban modelling geographical visualisation; geodesign; GIS

Special Issue Information

Dear Colleagues,

The trends in global population growth by international organisations such as the United Nations indicate that the number of people living in cities and urban areas will increase dramatically on current figures in the next decade. These population growth challenges will require global societal responses, the results of which will determine the quality of life of billions of citizens. As it stands, humanity is already using the Earth's resources at an unsustainable rate and subsequently placing increasing pressures on the natural environment and its ecosystem upon which we so greatly depend. With these challenges comes unprecedented opportunities to collect spatial data and information about these cities and urban areas in order to address these problems. Big Data is an emerging research area which has immense potential to help society adapt to population increases and growing urbanization. Big Data is generated by a myriad of sources including satellites, in-situ sensor networks, sensing devices, Internet of Things systems and applications, the social Internet including social media, networking, etc. Indeed, more specifically, Geospatial Big Data refers to all of these data and information streams which contain specific spatial and location references. Citizens generate information and data which combines to make Geospatial Big Data in so many ways—passively (using apps, web sites, sensors, etc., automatically with little or no interaction) and actively with more interaction such as sharing GPS tracks, geolocating social media posts, contributing to Volunteered Geographic Information projects, etc. A massive challenge has arisen to elicit and extract knowledge and intelligence from this Geospatial Big Data in order to understand, predict and manage how cities and urban areas function, change and grow. Cities and urban areas themselves generate massive volumes of Geospatial Big Data directly and indirectly, passively and actively. However, few examples exist where these data resources have been efficiently explored and exploited in Urban and City Studies.

On the other hand, urban studies involve sophisticated factors which, once deployed, are expected to guide towards city smartness and urban smartness. These factors can be more effective using geospatial technology, and spatially enabled Big Data is definitely one of current and future trends of urban studies besides other implementation areas. Thus, considering environmental; physical; social; economic and other urban study factors which include but are not limited to location and geographic aspects such as lakes, seashores, hills, topography, geology and geomorphology, geographic needs, climate changes, current and future land use, socioeconomic level, future economic trends, say of citizens will require the involvement of Geospatial Big Data. Further, extracting valuable information from Geospatial Big Data using analytics, geo analytics spatial analysis, or perhaps the combination of both shall obligate the enforcement of quality dimensions of data and information. Those dimensions will increase the level of confidence while implementing it for urban development, urban planning and miscellaneous urban studies and city smartness and development. Dimensions such as data source reliability; data accuracy and precision as compared to the real values; data completeness and the effect of lacking part of the data on decision making; the uncertainty level of parts of Geospatial Big Data that is related to the fitness of use where the purpose of data extraction is to be deeply involved in the process; and other dimensions can be common in Big Data, etc. Thus, ideas and practical aspects of Geospatial reliability, enabling Big Data for urban studies, will be welcome herein.

This Special Issue is dedicated to exploring the methodologies and research techniques developed and delivered to tackle the challenges of Geospatial Big Data in order to understand how City Studies will benefit from it. We call for original papers which focus on all topics involving Geospatial Big Data and related City and Urban Studies.

The Special Issue will place emphasis on innovative research on Geospatial Big Data and approaches and methodologies to using this data. Example topics may include, but are not limited to, the following:

  • Data models for the representation of Geospatial Big Data
  • Geospatial Big Data analytics and knowledge discovery
  • Quality assessment of Geospatial Big Data
  • Examples of City and Urban Studies on Geospatial Big Data
  • City Analytics methods and applications
  • City Dashboards for spatial planning and citizen engagement
  • Urban data visualisation including virtual and augmented reality

Prof. Dr. Maria Antonia Brovelli
Mr. Hussein M. Abdulmuttalib
Dr. Peter Mooney
Prof. Chris Pettit
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.

Keywords

  • Urban Big data
  • Sensors/Internet of Things
  • Geo Crowdsourced Data
  • Social Web
  • Cities Studies
  • City analytics
  • Geographical visualisation
  • Virtual/Augmented reality
  • Dashboards
  • Urban Data Quality

Published Papers (12 papers)

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Research

Open AccessArticle A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data
ISPRS Int. J. Geo-Inf. 2018, 7(6), 203; https://doi.org/10.3390/ijgi7060203
Received: 26 April 2018 / Revised: 21 May 2018 / Accepted: 27 May 2018 / Published: 29 May 2018
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Abstract
Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines,
[...] Read more.
Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station
ISPRS Int. J. Geo-Inf. 2018, 7(5), 178; https://doi.org/10.3390/ijgi7050178
Received: 26 March 2018 / Revised: 30 April 2018 / Accepted: 7 May 2018 / Published: 8 May 2018
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Abstract
Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data.
[...] Read more.
Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data. First, the proposed method extracted spatial information of the gas station from sparse vehicle trace data in two steps. The first step proposed the velocity sequence linear clustering algorithm to extract refueling stop tracks from the individual trace line after modeling the vehicle refueling stop behavior using movement features. The second step used the Delaunay triangulation to extract the spatial information of gas stations from the collective refueling stop tracks. Second, attribute information and dimension sentiment semantic information of the gas station were extracted from social media data using the text mining method and tripartite graph model. Third, the gas station information was enhanced by fusing the extracted spatial data and semantic data using a matching method. Experiments were conducted using the 15-day vehicle trajectories of 12,000 taxis and social media data from the Dazhongdianping in Beijing, China, and the results showed that the proposed method could extract the spatial information, attribute information, and review information of gas stations simultaneously. Compared with ground truth data, the automatically enhanced gas station was proved to be of higher quality in terms of the correctness, completeness, and real-time. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data
ISPRS Int. J. Geo-Inf. 2018, 7(5), 177; https://doi.org/10.3390/ijgi7050177
Received: 10 March 2018 / Revised: 3 May 2018 / Accepted: 7 May 2018 / Published: 8 May 2018
PDF Full-text (6059 KB) | HTML Full-text | XML Full-text
Abstract
Taxi behavior is a spatial–temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In
[...] Read more.
Taxi behavior is a spatial–temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent’s parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent’s parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Stakeholder Specific Multi-Scale Spatial Representation of Urban Building-Stocks
ISPRS Int. J. Geo-Inf. 2018, 7(5), 173; https://doi.org/10.3390/ijgi7050173
Received: 1 April 2018 / Revised: 27 April 2018 / Accepted: 30 April 2018 / Published: 4 May 2018
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Abstract
Urban building-stocks use a significant amount of resources and energy. At the same time, they have a large potential for energy efficiency measures (EEM). To support decision-making and planning, spatial building-stock models are used to examine the current state and future development of
[...] Read more.
Urban building-stocks use a significant amount of resources and energy. At the same time, they have a large potential for energy efficiency measures (EEM). To support decision-making and planning, spatial building-stock models are used to examine the current state and future development of urban building-stocks. While these models normally focus on specific cities, generic and broad stakeholder groups such as planners and policy makers are often targeted. Consequently, the visualization and communication of results are not tailored to these stakeholders. The aim of this paper is to explore the possibilities of mapping and representing energy use of urban building-stocks at different levels of aggregation and spatial distributions, to communicate with specific stakeholders involved in the urban development process. This paper uses a differentiated building-stock description based on building-specific data and measured energy use from energy performance certificates for multi-family buildings (MFB) in the city of Gothenburg. The building-stock description treats every building as unique, allowing results to be provided at any level of aggregation to suit the needs of the specific stakeholders involved. Calculated energy use of the existing stock is within 10% of the measured energy use. The potential for EEM in the existing stock is negated by the increased energy use due to new construction until 2035, using a development scenario based on current renovation rates and planned developments. Visualizations of the current energy use of the stock as well as the impact of renovation and new construction are provided, targeting specific local stakeholders. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories
ISPRS Int. J. Geo-Inf. 2018, 7(4), 128; https://doi.org/10.3390/ijgi7040128
Received: 4 February 2018 / Revised: 10 March 2018 / Accepted: 17 March 2018 / Published: 21 March 2018
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Abstract
Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating
[...] Read more.
Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating strategies. Most existing methods for analyzing urban congestion patterns are based on traditional traffic detector data, which is inflexible and expensive. Additionally, prior research primarily focused on the microscopic model, which simulated congestion propagation based on theoretical models and hypothetical networks. As such, most previous models and methods are difficult to apply to real case scenarios. Therefore, we investigated recurrent congestion patterns by mining historical taxi trajectory data that were collected in Harbin, China. A three-step method is proposed to reveal urban recurrent congestion evolution patterns. Firstly, a grid-based congestion detection method is presented by calculating the change in taxi global positioning system (GPS) trajectory patterns. Secondly, a customized cluster algorithm is applied to measure the recurrent congestion area. Finally, a series of indicators are proposed to reflect RC evolution patterns. A case study was competed in the Harbin urban area to evaluate the main methods. Finally, RC cause analysis and alleviating strategy are discussed. The results study are expected to provide a better understanding of urban RC evolution patterns. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 99; https://doi.org/10.3390/ijgi7030099
Received: 29 January 2018 / Revised: 28 February 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
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Abstract
Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country
[...] Read more.
Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle The Application of the Analytic Hierarchy Process and a New Correlation Algorithm to Urban Construction and Supervision Using Multi-Source Government Data in Tianjin
ISPRS Int. J. Geo-Inf. 2018, 7(2), 50; https://doi.org/10.3390/ijgi7020050
Received: 1 December 2017 / Revised: 15 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
PDF Full-text (1306 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most
[...] Read more.
As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most studies involving big data address applications in scientific, commercial, and ecological fields. However, the application of big data to government management is also needed. This paper examines the application of multi-source government data to urban construction and supervision in Tianjin, China. The analytic hierarchy process and a new approach called the correlation degree algorithm are introduced to calculate the degree of correlation between different approval items in one construction project and between different construction projects. The results show that more than 75% of the construction projects and their approval items are highly correlated. The results of this study suggest that most of the examined construction projects are well supervised, have relatively high probabilities of satisfying the relevant legal requirements, and observe their initial planning schemes. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture
ISPRS Int. J. Geo-Inf. 2018, 7(1), 26; https://doi.org/10.3390/ijgi7010026
Received: 6 November 2017 / Revised: 9 January 2018 / Accepted: 11 January 2018 / Published: 15 January 2018
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Abstract
The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario,
[...] Read more.
The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model
ISPRS Int. J. Geo-Inf. 2017, 6(11), 373; https://doi.org/10.3390/ijgi6110373
Received: 26 September 2017 / Revised: 2 November 2017 / Accepted: 13 November 2017 / Published: 19 November 2017
Cited by 1 | PDF Full-text (7864 KB) | HTML Full-text | XML Full-text
Abstract
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next
[...] Read more.
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement
ISPRS Int. J. Geo-Inf. 2017, 6(7), 212; https://doi.org/10.3390/ijgi6070212
Received: 4 May 2017 / Revised: 2 July 2017 / Accepted: 5 July 2017 / Published: 14 July 2017
Cited by 1 | PDF Full-text (3124 KB) | HTML Full-text | XML Full-text
Abstract
Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety
[...] Read more.
Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety and complexity of people’s movement activity and the large size of trajectory datasets make it difficult to mine valuable trajectory patterns. Moreover, most existing trajectory similarity measurements only consider a portion of the information contained in trajectory data. The patterns obtained cannot be interpreted well in terms of both semantic meaning and geographic distributions. As a result, these patterns cannot be used accurately for recommendation systems or other applications. This paper introduces a novel concept of the semantic-geographic pattern that considers both semantic and geographic meaning simultaneously. A flexible density-based clustering algorithm with a new trajectory similarity measurement called semantic intensity is used to mine these semantic-geographic patterns. Comparative experiments on check-in data from the Sina Weibo service demonstrate that semantic intensity can effectively measure both semantic and geographic similarities among trajectories. The resulting patterns are more accurate and easy to interpret. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle A Visual Analysis Approach for Inferring Personal Job and Housing Locations Based on Public Bicycle Data
ISPRS Int. J. Geo-Inf. 2017, 6(7), 205; https://doi.org/10.3390/ijgi6070205
Received: 2 May 2017 / Revised: 22 June 2017 / Accepted: 29 June 2017 / Published: 7 July 2017
PDF Full-text (2681 KB) | HTML Full-text | XML Full-text
Abstract
Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on
[...] Read more.
Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on human mobility data. However, because the travel patterns of residents are variable, simple rule-based methods are unable to generalize highly changing and complex travel modes. In this paper, we propose a visual analysis approach to assist the analyzer in inferring personal job and housing locations interactively based on public bicycle data. All users are first clustered to find potential commuting users. Then, several visual views are designed to find the key candidate stations for a specific user, and the visited temporal pattern of stations and the user’s hire behavior are analyzed, which helps with the inference of station semantic meanings. Finally, a number of users’ job and housing locations are detected by the analyzer and visualized. Our approach can manage the complex and diverse cycling habits of users. The effectiveness of the approach is shown through case studies based on a real-world public bicycle dataset. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle A New Look at Public Services Inequality: The Consistency of Neighborhood Context and Citizens’ Perception across Multiple Scales
ISPRS Int. J. Geo-Inf. 2017, 6(7), 200; https://doi.org/10.3390/ijgi6070200
Received: 2 May 2017 / Revised: 26 June 2017 / Accepted: 29 June 2017 / Published: 4 July 2017
Cited by 1 | PDF Full-text (1544 KB) | HTML Full-text | XML Full-text
Abstract
A challenge in regional inequality is to identify the relative influence of objective neighborhood context on subjective citizens’ attitudes and experiences of place. This paper first presents six groups of hierarchal neighborhoods in optimizing public service inequality (PSI) indicators based on census blocks
[...] Read more.
A challenge in regional inequality is to identify the relative influence of objective neighborhood context on subjective citizens’ attitudes and experiences of place. This paper first presents six groups of hierarchal neighborhoods in optimizing public service inequality (PSI) indicators based on census blocks collected in Quito, Ecuador. Multilevel models were then applied to understand the relative influence of neighborhood-level PSI on citizens’ perceptions of place, including individual-level perceptions of neighborhood social cohesion and neighborhood safety, and self-perceived health status. Our results show that the internal variability of the individual perceptions that is explained by neighborhood context is strongly influenced by the scale of neighborhood units. A spatial consistency between objective neighborhood context and subjective individual perception of place plays a crucial role in propagating mixed-methods approaches (qualitative-quantitative) and improves the spatial interpretation of public services inequality. Neighborhood context and citizens’ perception of place should be integrated to investigate urban segregation, thereby providing insights into the underlying societal inequality phenomenon and quality of life. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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