Special Issue "Human-Centric Data Science for Urban Studies"

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

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Assist. Prof. Bernd Resch

1. Department of Geoinformatics, Paris-Lodron University of Salzburg, Austria
2. Center for Geographic Analysis, Harvard University, USA
Website | E-Mail
Interests: fusion of human and technical sensors; people as sensors and collective sensing (VGI); standardized geo-sensor webs; real-time; smart cities
Guest Editor
Assist. Prof. Michael Szell

Center for Network Science, Central European University (CEU), Hungary
Website | E-Mail
Interests: quantifying human behavior and social networks, in particular online and in cities; urban sustainability and human mobility; data visualization; data science; complex systems

Special Issue Information

Dear Colleagues,

In the past decade, the concept of smart cities has been greatly driven by the idea of an IT-infused city, that is, an urban system enriched with a number of different information technologies to support urban management and planning. However, most previous smart city research initiatives have promoted techno-positivistic approaches, which oftentimes do not account enough for the citizens' needs. Thus, this Special Issue focuses on a more human-centric view of smart cities. A variety of large-scale datasets, sensing technologies, geo-participation initiatives, collaborative mapping tools, and data science approaches have emerged that have the potential to help us in gaining a better understanding of urban processes and how to convert them into concrete urban planning and management actions. These new developments have led to a previously unknown situation in urban science, namely the transformation from data-scarce to data-rich research environments. To optimally leverage these new datasets and technologies, the GIScience community is currently developing innovative methods that go well beyond traditional geospatial analysis, including multidisciplinary approaches combining methods from GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o. This Special Issue encourages the submission of both basic research papers and application-oriented contributions in the area of urban data science, dedicating a particular focus to human-centric approaches.

DATA SOURCES

  • Human sensing technologies
  • Social media and VGI
  • Mobile phone networks
  • OSM and OGD
  • Participatory geo-technologies

METHODS

  • Spatio-temporal analysis of urban processes
  • Geo-infused self-learning systems and machine learning approaches
  • Statistical analysis of urban processes and structures
  • Sentiment analysis and emotion extraction
  • Dynamic, spatio-temporal geovisualisation
  • Multidisciplinary research (GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o.)

APPLICATION AREAS

  • Urban planning and management
  • Mobility and transportation
  • Wellbeing, quality of life and livability
  • Energy infrastructure planning and management

Assist. Prof. Bernd Resch
Assist. Prof. Michael Szell
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 (11 papers)

View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:

Research

Open AccessArticle Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data
ISPRS Int. J. Geo-Inf. 2018, 7(12), 459; https://doi.org/10.3390/ijgi7120459
Received: 25 September 2018 / Revised: 12 November 2018 / Accepted: 22 November 2018 / Published: 27 November 2018
PDF Full-text (8055 KB) | HTML Full-text | XML Full-text
Abstract
Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However,
[...] Read more.
Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation
ISPRS Int. J. Geo-Inf. 2018, 7(11), 416; https://doi.org/10.3390/ijgi7110416
Received: 21 August 2018 / Revised: 10 October 2018 / Accepted: 27 October 2018 / Published: 29 October 2018
PDF Full-text (3150 KB) | HTML Full-text | XML Full-text
Abstract
This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place, but also has causal influence on the number of such individuals. By creating an index
[...] Read more.
This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place, but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Analyzing OpenStreetMap Road Data and Characterizing the Behavior of Contributors in Ankara, Turkey
ISPRS Int. J. Geo-Inf. 2018, 7(10), 400; https://doi.org/10.3390/ijgi7100400
Received: 31 July 2018 / Revised: 18 September 2018 / Accepted: 4 October 2018 / Published: 6 October 2018
Cited by 1 | PDF Full-text (3341 KB) | HTML Full-text | XML Full-text
Abstract
The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and
[...] Read more.
The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Mapping Frictions Inhibiting Bicycle Commuting
ISPRS Int. J. Geo-Inf. 2018, 7(10), 396; https://doi.org/10.3390/ijgi7100396
Received: 16 July 2018 / Revised: 11 September 2018 / Accepted: 27 September 2018 / Published: 3 October 2018
PDF Full-text (11534 KB) | HTML Full-text | XML Full-text
Abstract
Urban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset
[...] Read more.
Urban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental, mobile geo-game. Fifty-seven participants recorded bicycle trips during one week periods in three cities. By aggregating them, we extracted not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, participants recorded bicycle segments at lower speeds indicating a deviation from an ideal cycling scenario. We describe the potential frictions inhibiting bicycle commuting with regard to the distance to bicycle paths, surrounding infrastructure, and location in the urban area. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data
ISPRS Int. J. Geo-Inf. 2018, 7(9), 378; https://doi.org/10.3390/ijgi7090378
Received: 17 August 2018 / Revised: 11 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
PDF Full-text (12197 KB) | HTML Full-text | XML Full-text
Abstract
Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents
[...] Read more.
Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Graphical abstract

Open AccessArticle Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender
ISPRS Int. J. Geo-Inf. 2018, 7(9), 358; https://doi.org/10.3390/ijgi7090358
Received: 15 June 2018 / Revised: 25 August 2018 / Accepted: 27 August 2018 / Published: 29 August 2018
Cited by 1 | PDF Full-text (6140 KB) | HTML Full-text | XML Full-text
Abstract
The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics
[...] Read more.
The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110). Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects
ISPRS Int. J. Geo-Inf. 2018, 7(8), 325; https://doi.org/10.3390/ijgi7080325
Received: 25 June 2018 / Revised: 27 July 2018 / Accepted: 6 August 2018 / Published: 12 August 2018
PDF Full-text (1479 KB) | HTML Full-text | XML Full-text
Abstract
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western
[...] Read more.
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach
ISPRS Int. J. Geo-Inf. 2018, 7(8), 308; https://doi.org/10.3390/ijgi7080308
Received: 18 June 2018 / Revised: 24 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
PDF Full-text (4926 KB) | HTML Full-text | XML Full-text
Abstract
Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to
[...] Read more.
Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Grid-Based Crime Prediction Using Geographical Features
ISPRS Int. J. Geo-Inf. 2018, 7(8), 298; https://doi.org/10.3390/ijgi7080298
Received: 24 May 2018 / Revised: 21 June 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
Cited by 1 | PDF Full-text (8960 KB) | HTML Full-text | XML Full-text
Abstract
Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the
[...] Read more.
Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces
ISPRS Int. J. Geo-Inf. 2018, 7(5), 190; https://doi.org/10.3390/ijgi7050190
Received: 10 April 2018 / Revised: 11 May 2018 / Accepted: 12 May 2018 / Published: 15 May 2018
PDF Full-text (8326 KB) | HTML Full-text | XML Full-text
Abstract
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public
[...] Read more.
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty
ISPRS Int. J. Geo-Inf. 2018, 7(2), 77; https://doi.org/10.3390/ijgi7020077
Received: 20 December 2017 / Revised: 6 February 2018 / Accepted: 18 February 2018 / Published: 24 February 2018
Cited by 2 | PDF Full-text (3162 KB) | HTML Full-text | XML Full-text
Abstract
The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid
[...] Read more.
The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Back to Top