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Special Issue "Human-Oriented Observation for Supporting Effective Decision-Making in Governance and Public Service"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3684

Special Issue Editors

Prof. Dr. Zhixiang Fang
E-Mail Website
Guest Editor
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: space–time GIS; smart cities; spatiotemporal optimization; intelligent logistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ling Yin
E-Mail Website
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: spatiotemporal data analysis; geographically computational epidemiology; GIS for transportation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean-Claude Thill
E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: transportation and mobility systems; accessibility; megaregions; spatial analysis; innovations; social media and social network analysis; urban dynamics; innovation; globalization; land markets and housing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our world inevitably faces various public crises, including severe events in public security, public health (i.e., COVID-19), mass transportation, or other public service, which cause considerable economic loss, social instability, environment damage, even the weakening of confidence in society and governance, and loss of life. Local governments need timely and reliable human observation technologies and AI-aided decision-making tools to response to these public crises by integrating various data sources such as remote sensing data, human location and communication data, social media data, surveillance and distributed sensor data, and so on. Remote sensing (RS) is effective in sensing environmental changes, but it falls short in observing human dynamics, which is a critical factor in supporting effective decision-making in governance and public management. Fortunately, geospatial information technologies (i.e., Global positioning system--Beidou/GPS, indoor positioning system, Automatic identification System and mobile communication system) provide a generalized human observation network, which could support the sensing and observation of human activities and their dynamics. However, challenges still exist in integrating multi-source human observation data for supporting effective decision-making in governance and public management, such as data quality, data representativeness, privacy issues, effectiveness of observation, data-driven modelling and smart decision-making in spatiotemporal processes. Therefore, it is urgent to develop better solutions to respond to public crises in a timely and effective manner by considering important aspects such as the requirements of governance and public management, sensing capabilities of RS and spatial analytics capabilities of geographic information science.

This Special Issue aims to explore new solutions in Human-oriented observation and smart decision-making for friendly governance and public management. In this context contributions that address, but are not restricted to, the following topics are welcome:

  • Sensing human activities from various RS imagery;
  • Observing human activities by integrating geospatial location data and social media data;
  • Modeling the dynamics of human activity progress;
  • Assessing the effect of human dynamics on social, economic and environmental systems;
  • Predicting human dynamics under different scenarios of public security, public health, public transportation and other public services;
  • Creating smart decision-making tools and solutions for governance and services;
  • Applications of Remote Sensing and other human observation techniques for public response of intelligent governance.

Submitted papers should present novel contributions and innovative applications. Relevant topical reviews are also welcome.

Prof. Dr. Zhixiang Fang
Dr. Ling Yin
Prof. Dr. Jean-Claude Thill
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 submissions that pass pre-check are 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2500 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 (4 papers)

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Research

Article
Exploring Park Visit Variability Using Cell Phone Data in Shenzhen, China
Remote Sens. 2022, 14(3), 499; https://doi.org/10.3390/rs14030499 - 21 Jan 2022
Cited by 1 | Viewed by 608
Abstract
Exploring the spatiotemporal characteristics of park visitors and the “push and pull” factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. [...] Read more.
Exploring the spatiotemporal characteristics of park visitors and the “push and pull” factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. First, cell phone signaling data were used to extract information on visitors’ departure locations and destination parks. Second, the bivariate Moran’s I and bivariate local Moran’s I (BiLISA) methods were used to identify the statistical correlation between the factors of the built environment and the park recreation trips. Finally, linear regression models were constructed to quantify the factors influencing the attractiveness of the park. Our study showed the following: (1) Recreation visitors at large parks varied significantly among population subgroups. Compared with younger adults, teenagers and older adults traveled lower distances and made fewer trips, and in particular, older adults of different genders differed significantly in park participation. (2) Recreational trips in large parks were related to the functional layout of the built environment around their residence. In areas with rich urban functions (e.g., southern Shenzhen), trips to large parks for leisure are more aggregated. (3) The findings reinforce the evidence that remote sensing data for urban vegetation can be an effective factor in characterizing park attractiveness, but the explanatory power of different vegetation data varies widely. Our study integrated the complementary human activity and remote sensing data to provide a more comprehensive understanding of urban park use and preferences. This will be important for future park planning. Full article
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Article
Evaluating the Impact of Grazing Cessation and Reintroduction in Mixed Prairie Using Raster Time Series Analysis of Landsat Data
Remote Sens. 2021, 13(17), 3397; https://doi.org/10.3390/rs13173397 - 27 Aug 2021
Viewed by 660
Abstract
Great efforts have been made to manage and restore native prairies to protect native species, enrich biodiversity, protect ecological resilience, and maintain ecosystem services. Much of this has been focused on preventing degradation from overgrazing and crop conversion. Understanding the consequences of management [...] Read more.
Great efforts have been made to manage and restore native prairies to protect native species, enrich biodiversity, protect ecological resilience, and maintain ecosystem services. Much of this has been focused on preventing degradation from overgrazing and crop conversion. Understanding the consequences of management polices is important to identify best practices. Previous research has compared restoration outcomes from variable intensity grazing, prescribed fire, and grazing removal. However, few studies have explored the optimal durations of management practices and variation in restoration outcomes among vegetation communities. This study evaluates whether the impact of grazing cessation and reintroduction varies among native vegetation communities and measures the effective time periods of grazing cessation and reintroduction. Restoration outcomes were evaluated using four biophysical indicators (fresh biomass, soil organic matter, green cover, and litter cover) and two vegetation indices (normalized difference vegetation index (NDVI) and normalized difference water index (NDWI)) measured from Landsat images using seasonal Kalman filter and raster time series analysis. The results show that: (i) Grazing cessation increased soil organic matter and green cover while decreasing fresh biomass compared to moderate grazing management, while grazing reintroduction influences those indicators in an opposite direction; (ii) The effective time period for prairie conservation is about 11–14 years and varies among vegetation communities and biophysical indicators; (iii) The effective intensity of grazing cessation is highest in valley grassland, moderate in upland grassland, and mildest in sloped grassland; (iv) Grazing reintroduction returned the three native vegetation communities to the initial condition (i.e., the stage in 1985 before large grazers were removed), with less time than the time consumed for grazing cessation to restore the prairie ecosystem to the maximum changes; (v) Grazing reintroduction effectively influences upland and valley grasslands for 7 to 9 years, varying from different indicators, while it continuously affected sloped grassland with no clear time lag; (vi) The intensity of grazing reintroduction was strongest in sloped grassland, moderate in upland grassland, and mildest in valley grassland. The results of this study suggest expected time periods for prairie management methods to achieve results. Full article
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Article
SRR-LGR: Local–Global Information-Reasoned Social Relation Recognition for Human-Oriented Observation
Remote Sens. 2021, 13(11), 2038; https://doi.org/10.3390/rs13112038 - 21 May 2021
Cited by 1 | Viewed by 743
Abstract
People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public administrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the [...] Read more.
People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public administrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the most efficient ways to observe human interactions in a public space. Generally speaking, persons in the same scene tend to know each other, and the relations between person pairs are strongly correlated. The scene information in which people interact is also one of the important cues for social relation recognition (SRR). The existing works have not explored the correlations between the scene information and people’s interactions. The scene information has only been extracted on a simple level and high level semantic features to support social relation understanding are lacking. To address this issue, we propose a social relation structure-aware local–global model for SRR to exploit the high-level semantic global information of the scene where the social relation structure is explored. In our proposed model, the graph neural networks (GNNs) are employed to reason through the interactions (local information) between social relations and the global contextual information contained in the constructed scene-relation graph. Experiments demonstrate that our proposed local–global information-reasoned social relation recognition model (SRR-LGR) can reason through the local–global information. Further, the results of the final model show that our method outperforms the state-of-the-art methods. In addition, we have further discussed whether the global information contributes equally to different social relations in the same scene, by exploiting an attention mechanism in our proposed model. Further applications of SRR for human-observation are also exploited. Full article
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Article
Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective
Remote Sens. 2021, 13(9), 1825; https://doi.org/10.3390/rs13091825 - 07 May 2021
Viewed by 705
Abstract
Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to [...] Read more.
Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research. Full article
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