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: closed (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
Prof. Michael Szell

Computer Science Department, IT University of Copenhagen, Rued Langgaards Vej 7, 2300 København, Denmark
Website | E-Mail
Interests: human mobility; urban sustainability; transport justice; social networks; network science; data science; data visualization

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 (24 papers)

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Open AccessArticle
Spatio-Temporal Change Characteristics of Spatial-Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China
ISPRS Int. J. Geo-Inf. 2019, 8(6), 273; https://doi.org/10.3390/ijgi8060273
Received: 26 April 2019 / Revised: 24 May 2019 / Accepted: 5 June 2019 / Published: 12 June 2019
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Abstract
Spatial-interaction networks are an important factor in geography that could help in the exploration of both human spatial-temporal behavior and the structure of urban areas. This paper analyzes changes in the spatio-temporal characteristics of the Spatial-Interaction Networks of Beijing (SINB) in three consecutive [...] Read more.
Spatial-interaction networks are an important factor in geography that could help in the exploration of both human spatial-temporal behavior and the structure of urban areas. This paper analyzes changes in the spatio-temporal characteristics of the Spatial-Interaction Networks of Beijing (SINB) in three consecutive steps. To begin with, we constructed 24 sequential snapshots of spatial population interactions on the basis of points of interest (POIs) collected from Dianping.com and various taxi GPS data in Beijing. Then, we used Jensen–Shannon distance and hierarchical clustering to integrate the 24 sequential network snapshots into four clusters. Finally, we improved the weighted k-core decomposition method by combining the complex network method and weighted distance in a geographic space. The results showed: (1) There are three layers in the SINB: a core layer, a bridge layer, and a periphery layer. The number of places greatly varies, and the SINB show an obvious hierarchical structure at different periods. The core layer contains fewer places that are between the Second and Fifth Ring Road in Beijing. Moreover, spatial distribution of places in the bridge layer is always in the same location as that of the core layer, and the quantity in the bridge layer is always superior to that in the core layer. The distributions of places in the periphery layer, however, are much greater and wider than the other two layers. (2) The SINB connected compactly over time, bearing much resemblance to a small-world network. (3) Two patterns of connection, each with different connecting ratios between layers, appear on weekdays and weekends, respectively. Our research plays a vital role in understanding urban spatial heterogeneity, and helps to support decisions in urban planning and traffic management. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Evaluating Urban Bicycle Infrastructures through Intersubjectivity of Stress Sensations Derived from Physiological Measurements
ISPRS Int. J. Geo-Inf. 2019, 8(6), 265; https://doi.org/10.3390/ijgi8060265
Received: 30 April 2019 / Revised: 29 May 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
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Abstract
A continued shift of human mobility towards sustainable and active mobility modes is a major concern for society in order to reduce the human contribution to climate change as well as to improve liveability and health in urban environments. For this change to [...] Read more.
A continued shift of human mobility towards sustainable and active mobility modes is a major concern for society in order to reduce the human contribution to climate change as well as to improve liveability and health in urban environments. For this change to succeed, non-motorized modes of transport need to become more attractive. Cycling can play a substantial role for short to medium distances, but perceived safety and stress levels are still major concerns for cyclists. Therefore, a quantitative assessment of cyclists’ stress sensations constitutes a valuable input for urban planning and for optimized routing providing low-stress routes. This paper aims to investigate stress sensations of cyclists through quantifying physiological measurements and their spatial correlation as an intersubjective indicator for perceived bikeability. We developed an automated workflow for stress detection and aggregation, and validated it in a case study in the city of Salzburg, Austria. Our results show that measured stress generally matches reported stress perception and can thus be considered a valuable addition to mobility planning processes. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City
ISPRS Int. J. Geo-Inf. 2019, 8(6), 249; https://doi.org/10.3390/ijgi8060249
Received: 13 March 2019 / Revised: 12 May 2019 / Accepted: 25 May 2019 / Published: 29 May 2019
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Abstract
The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 [...] Read more.
The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
The Spatial Equity of Nursing Homes in Changchun: A Multi-Trip Modes Analysis
ISPRS Int. J. Geo-Inf. 2019, 8(5), 223; https://doi.org/10.3390/ijgi8050223
Received: 25 January 2019 / Revised: 2 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
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Abstract
Based on network analysis, different trip modes were integrated into an improved potential model, and the geography of the spatial equity of nursing homes in Changchun is explored in 5-min, 10-min and 15-min scenarios, respectively. Results show that: (1) trip modes have significant [...] Read more.
Based on network analysis, different trip modes were integrated into an improved potential model, and the geography of the spatial equity of nursing homes in Changchun is explored in 5-min, 10-min and 15-min scenarios, respectively. Results show that: (1) trip modes have significant influence on spatial equity and that the geography of spatial equity varied with trip modes; (2) the spatial equity value in Changchun is overall kept to a very low level. Most areas in urban fringes and urban core areas belong to underserved areas, and the capacity of nursing home, travel cost and the number of seniors, are the main influencing factors; (3) the geography of spatial equity in different scenarios show a very similar ring structure; namely, the spatial equity value within the urban core and at the most urban periphery is lower than that in intermediate areas. The hot spot analysis showed that the southwest urban fringes and east of the urban core are hot spot areas, while the urban core itself has cold spot areas. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data
ISPRS Int. J. Geo-Inf. 2019, 8(5), 214; https://doi.org/10.3390/ijgi8050214
Received: 3 March 2019 / Revised: 9 April 2019 / Accepted: 4 May 2019 / Published: 7 May 2019
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Abstract
Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the [...] Read more.
Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the applicability of BLOS rating schemes for the estimation of whole-journey route choice. The methods evaluated share a common trait of being applicable for mixed traffic urban environments: Bicycle Compatibility Index (BCI), Bicycle Stress Level (BSL), Sixth Edition Highway Capacity Manual (HCM6), and Level of Traffic Stress (LTS). Routes are generated based on BLOS-weighted networks and the suitability of these routes is determined by finding the percentage overlap with empirical route choices. The results show that BCI provides the best match with empirical route data in all five origin–destination pairs, followed by HCM6. BSL and LTS which are not empirically founded have a lower match rate, although the differences between the four methods are relatively small. By iterating the detour rate that cyclists are assumed to be willing to make, it is found that the best match with modelled BLOS routes is achieved between 15 and 21% additional length. This falls within the range suggested by existing empirical research on willingness to deviate from the shortest path, however, it is uncertain whether the method will deliver the comparable findings in other cycling environments. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Hot Spot Analysis versus Cluster and Outlier Analysis: An Enquiry into the Grouping of Rural Accommodation in Extremadura (Spain)
ISPRS Int. J. Geo-Inf. 2019, 8(4), 176; https://doi.org/10.3390/ijgi8040176
Received: 13 February 2019 / Revised: 8 March 2019 / Accepted: 1 April 2019 / Published: 4 April 2019
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Abstract
The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, [...] Read more.
The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, by means of a geographical information system (GIS) and spatial statistics, that it is possible to better define the groupings of rural accommodation available in Extremadura, Spain, especially if these are conceptualized by dint of their lodging capacity. To do so, two specific techniques have been used: hotspot analysis and outlier analysis, which yield results that prove the existence of homogeneous and heterogeneous groups of accommodation businesses, based not only on their spatial proximity but also on their lodging capacity. On the basis of this analysis, the regional administration can devise tourist policies and strategic plans in order to improve the management and efficiency of each business. Despite the applicability of the present results, this study also addresses the difficulties in using these techniques—Where establishing the spatial relationships and the boundary distance are key concepts. In the case study here, the ideal configuration utilizes a fixed distance of six miles. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Large-Scale Station-Level Crowd Flow Forecast with ST-Unet
ISPRS Int. J. Geo-Inf. 2019, 8(3), 140; https://doi.org/10.3390/ijgi8030140
Received: 24 January 2019 / Revised: 7 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
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Abstract
High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public [...] Read more.
High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
ISPRS Int. J. Geo-Inf. 2019, 8(2), 76; https://doi.org/10.3390/ijgi8020076
Received: 30 November 2018 / Revised: 13 January 2019 / Accepted: 27 January 2019 / Published: 5 February 2019
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Abstract
Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is [...] Read more.
Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is a fundamental tool to evaluate transient outdoor comfort. This study explains the prototyping and validation of a mobile environmental sensor kit. The results show the prototype has reasonable accuracy despite its affordability with respect to industrial sensors. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Examining Trade-Offs between Social, Psychological, and Energy Potential of Urban Form
ISPRS Int. J. Geo-Inf. 2019, 8(2), 52; https://doi.org/10.3390/ijgi8020052
Received: 30 November 2018 / Revised: 1 January 2019 / Accepted: 20 January 2019 / Published: 24 January 2019
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Abstract
Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network [...] Read more.
Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Spatial–Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013
ISPRS Int. J. Geo-Inf. 2019, 8(1), 31; https://doi.org/10.3390/ijgi8010031
Received: 14 November 2018 / Revised: 9 January 2019 / Accepted: 10 January 2019 / Published: 15 January 2019
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Abstract
Quantifying the temporal and spatial patterns of impervious surfaces (IS) is important for assessing the environmental and ecological impacts of urbanization. In order to better extract IS, and to explore the divergence in urbanization in different regions, research on the regional differentiation features [...] Read more.
Quantifying the temporal and spatial patterns of impervious surfaces (IS) is important for assessing the environmental and ecological impacts of urbanization. In order to better extract IS, and to explore the divergence in urbanization in different regions, research on the regional differentiation features and regional change difference features of IS are required. To extract China’s 2013 urban impervious area, we used the 2013 night light (NTL) data and the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index and enhanced vegetation index (EVI) temporal series data, and used three urban impervious surface extraction indexes—Human Settlements Index, Vegetation-Adjusted NTL Urban Index, and the EVI-adjusted NTL index (EANTLI)—which are recognized as the best and most widely used indexes for extracting urban impervious areas. We used the classification results of the Landsat-8 images as the benchmark data to visually compare and verify the results of the urban impervious area extracted by the three indexes. We determined that the EANTLI index better reflects the distribution of the impervious area. Therefore, we used the EANTLI index to extract the urban impervious area from 2003 to 2013 in the study area, and researched the spatial and temporal differentiation in urban IS. The results showed that China’s urban IS area was 70,179.06 km2, accounting for 0.73% of the country’s land area in 2013, compared with 20,565.24 km2 in 2003, which accounted for 0.21% of the land area, representing an increase of 0.52%. On a spatial scale, like economic development, the distribution of urban impervious surfaces was different in different regions. The overall performance of the urban IS percentage was characterized by a decreasing trend from Northwest China, Southwest China, the Middle Reaches of the Yellow River, Northeast China, the Middle Reaches of the Yangtze River, Southern Coastal China, and Northern Coastal China to Eastern Coastal China. On the provincial scale, the urban IS expansion showed considerable differences in different regions. The overall performance of the Urban IS Expansion index showed that the eastern coastal areas had higher values than the western inland areas. The cities or provinces of Beijing, Tianjin, Jiangsu, and Shanghai had the largest growth in impervious areas. Spatially and temporally quantifying the change in urban impervious areas can help to better understand the intensity of urbanization in a region. Therefore, quantifying the change in urban impervious area has an important role in the study of regional environmental and economic development, policy formulation, and the rational use of resources in both time and space. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities
ISPRS Int. J. Geo-Inf. 2019, 8(1), 19; https://doi.org/10.3390/ijgi8010019
Received: 23 November 2018 / Revised: 21 December 2018 / Accepted: 7 January 2019 / Published: 9 January 2019
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Abstract
Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, [...] Read more.
Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle
Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data
ISPRS Int. J. Geo-Inf. 2018, 7(12), 480; https://doi.org/10.3390/ijgi7120480
Received: 1 October 2018 / Revised: 4 December 2018 / Accepted: 13 December 2018 / Published: 16 December 2018
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Abstract
Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. In this paper, a new centrality measure for complex networks is proposed based on two contrasting models that have their common origin in the well-known PageRank centrality. [...] Read more.
Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. In this paper, a new centrality measure for complex networks is proposed based on two contrasting models that have their common origin in the well-known PageRank centrality. On the one hand, the essence of the model proposed is taken from the Adapted PageRank Algorithm (APA) centrality, whose main characteristic is that constitutes a measure to establish a ranking of nodes considering the importance of some dataset associated to the network. On the other hand, a technique known as two-layers PageRank approach is applied to this model. This technique focuses on the idea that the PageRank centrality can be understood as a two-layer network, the topological and teleportation layers, respectively. The main point of the proposed centrality is that it combines the APA centrality with the idea of two-layers; however, the difference now is that the teleportation layer is replaced by a layer that collects the data present in the network. This combination gives rise to a new algorithm for ranking the nodes according to their importance. Subsequently, the coherence of the new measure is demonstrated by calculating the correlation and the quantitative differences of both centralities (APA and the new centrality). A detailed study of the differences of both centralities, taking different types of networks, is performed. A real urban network with data randomly generated is evaluated as well as the well-known Zachary’s karate club network. Some numerical results are carried out by varying the values of the α parameter—known as dumping factor in PageRank model—that varies the importance given to the two layers (topology and data) within the computation of the new centrality. The proposed algorithm takes the best characteristics of the models on which it is based: on the one hand, it is a measure of centrality, in complex networks with data, whose calculation is stable numerically and, on the other hand, it is able to separate the topological properties of the network and the influence of the data. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
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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)
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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
Cited by 1 | 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)
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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
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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)
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Open AccessCase Report
Analysis of Spatial Characteristics of Digital Signage in Beijing with Multi-Source Data
ISPRS Int. J. Geo-Inf. 2019, 8(5), 207; https://doi.org/10.3390/ijgi8050207
Received: 31 March 2019 / Revised: 20 April 2019 / Accepted: 2 May 2019 / Published: 6 May 2019
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Abstract
Digital signage is an important medium for urban outdoor advertising. Understanding the spatial distribution characteristics and factors that influence the site of digital signage are conducive to the efficient, standardized, and sustainable development of digital signage. The outdoor commercial digital signage within the [...] Read more.
Digital signage is an important medium for urban outdoor advertising. Understanding the spatial distribution characteristics and factors that influence the site of digital signage are conducive to the efficient, standardized, and sustainable development of digital signage. The outdoor commercial digital signage within the Sixth Ring Road in Beijing is taken as the research object, and social network check-ins, housing prices, traffic network centrality and the mount of commercial facilities are considered factors that influence digital signage. The spatial distribution characteristics of digital signage are studied by using point pattern analysis methods. Moreover, we use three spatial clustering algorithms to study the hierarchical spatial characteristics of digital signage and test the effectiveness of the results. In addition, the factors that influence the distribution of digital signage are analyzed by Spearman correlation analysis. The results indicate that (1) the digital signage in Beijing generally presents a relatively concentrated distribution with centrality and forms an obvious gathering area and the agglomeration centers are mainly concentrated in the core parts of the central business district (CBD). (2) Digital signage is categorized into three groups, the traffic-oriented, the population-oriented, and the market-oriented. In addition, the spatial distribution of digital signage is consistent with the historical urban development of Beijing. (3) The social network check-ins with dynamic population characteristics had the highest correlation with the operation cost of digital signage. The spatial characteristics of digital signage evaluated in this study can effectively enhance the sustainable management of digital signage and provide a reference for research of the sustainable allocation of digital signage resources. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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