The Experts below are selected from a list of 130491 Experts worldwide ranked by ideXlab platform
Laurent Beauguitte - One of the best experts on this subject based on the ideXlab platform.
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Spatial Science and Network Science: Review and Outcomes of a Complex Relationship
Networks and Spatial Economics, 2014Co-Authors: César Ducruet, Laurent BeauguitteAbstract:For decades, the Spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed. Conversely, the physics literature that developed the popular scale-free and small-world models pays an increasing attention to the Spatial dimension of networks. Reviewing how complex network research has been integrated into geography and regional Science reveals a high heterogeneity among Spatial scientists as well as key directions for increasing their role inside multidisciplinary researches on networks.
Boeing Geoff - One of the best experts on this subject based on the ideXlab platform.
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The Right Tools for the Job: The Case for Spatial Science Tool-Building
eScholarship University of California, 2020Co-Authors: Boeing GeoffAbstract:This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The Spatial Sciences have recently seen growing calls for more accessible software and tools that better embody geographic Science and theory. Urban Spatial network Science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban Spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this, then reviews the literature of multidisciplinary empirical Spatial network Science recently conducted using it to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social Science more broadly. To conduct better Science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data Science beyond mere reproducibility and replicability
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The Right Tools for the Job: The Case for Spatial Science Tool-Building
'Wiley', 2020Co-Authors: Boeing GeoffAbstract:This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The Spatial Sciences have recently seen growing calls for more accessible software and tools that better embody geographic Science and theory. Urban Spatial network Science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban Spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary Spatial network Science literature to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social Science more broadly. To conduct better Science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data Science beyond mere reproducibility and replicability
J A Sobrino - One of the best experts on this subject based on the ideXlab platform.
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satellite remote sensing of surface urban heat islands progress challenges and perspectives
Remote Sensing, 2018Co-Authors: Decheng Zhou, Jingfeng Xiao, Stefania Bonafoni, Christian Berger, Kaveh Deilami, Yuyu Zhou, Steve Frolking, Rui Yao, Zhi Qiao, J A SobrinoAbstract:The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with Spatial Science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large Spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.
César Ducruet - One of the best experts on this subject based on the ideXlab platform.
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Spatial Science and Network Science: Review and Outcomes of a Complex Relationship
Networks and Spatial Economics, 2014Co-Authors: César Ducruet, Laurent BeauguitteAbstract:For decades, the Spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed. Conversely, the physics literature that developed the popular scale-free and small-world models pays an increasing attention to the Spatial dimension of networks. Reviewing how complex network research has been integrated into geography and regional Science reveals a high heterogeneity among Spatial scientists as well as key directions for increasing their role inside multidisciplinary researches on networks.
Alexis Comber - One of the best experts on this subject based on the ideXlab platform.
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Opening practice: supporting reproducibility and critical Spatial data Science
Journal of Geographical Systems, 2020Co-Authors: Chris Brunsdon, Alexis ComberAbstract:This paper reflects on a number of trends towards a more open and reproducible approach to geographic and Spatial data Science over recent years. In particular, it considers trends towards Big Data, and the impacts this is having on Spatial data analysis and modelling. It identifies a turn in academia towards coding as a core analytic tool, and away from proprietary software tools offering ‘black boxes’ where the internal workings of the analysis are not revealed. It is argued that this closed form software is problematic and considers a number of ways in which issues identified in Spatial data analysis (such as the MAUP) could be overlooked when working with closed tools, leading to problems of interpretation and possibly inappropriate actions and policies based on these. In addition, this paper considers the role that reproducible and open Spatial Science may play in such an approach, taking into account the issues raised. It highlights the dangers of failing to account for the geographical properties of data, now that all data are Spatial (they are collected some where ), the problems of a desire for $$n$$ n = all observations in data Science and it identifies the need for a critical approach. This is one in which openness, transparency, sharing and reproducibility provide a mantra for defensible and robust Spatial data Science.
