scholarly journals Big Data, social physics, and spatial analysis: The early years

2014 ◽  
Vol 1 (1) ◽  
pp. 205395171453536 ◽  
Author(s):  
Trevor J Barnes ◽  
Matthew W Wilson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Early Years ◽  
Social Physics

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

scholarly journals A classic that wasn’t: Statistical Geography and paths only later taken

2019 ◽  
Vol 44 (2) ◽  
pp. 357-373 ◽  
Author(s):  
Ron Johnston ◽  
Kelvyn Jones
Keyword(s):  
Spatial Analysis ◽  
Early Years ◽  
Body Of Knowledge ◽  
Cumulative Activity

Science is a cumulative activity, a body of knowledge sedimented in its publications, which form the foundation for further activity. Some items attract more attention than others; some are largely ignored. This paper looks at a largely overlooked book – Statistical Geography – published by three US sociologists at a time when geographers were launching their ‘quantitative revolution’. There was little literature within the discipline on which that revolution could be based, and a book with that title could have been seminal. But it was not, and as a consequence – as illustrated with three examples – major issues in spatial analysis were not addressed in the revolution’s early years. The paper explores why.

scholarly journals High-Performance Geospatial Big Data Processing System Based on MapReduce

2018 ◽  
Vol 7 (10) ◽  
pp. 399 ◽  
Author(s):  
Junghee Jo ◽  
Kang-Woo Lee
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Data Processing ◽  
Spatial Data ◽  
High Performance ◽  
Rapid Development ◽  
Processing System ◽  
Data Processing System ◽  
Seamless Integration ◽  
Big Data Processing

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

Geographic information science II: Mesogeography

2017 ◽  
Vol 42 (4) ◽  
pp. 600-609 ◽  
Author(s):  
Harvey J. Miller
Keyword(s):  
Spatial Analysis ◽  
Information Science ◽  
Spatial Context ◽  
Social Phenomena ◽  
Science And Policy ◽  
Social Physics ◽  
Wide Range ◽  
Geographic Knowledge ◽  
The Cost ◽  
Interesting Behavior

The 20th century witnessed the rise of social physics: the application of models and techniques developed for physical processes to social phenomena. Social physics left an enduring legacy in human geography via its stepchildren, spatial analysis and GIS, shifting geography from microgeography (description-seeking) and towards macrogeography (law-seeking). Social physics is back in the 21st century, and its renaissance with a concurrent rise in computational and data-driven approaches to science and policy raises a wide range of concerns, including the claim that this is just macrogeography writ large: a single-minded pursuit of social laws at the cost of treating people as particles and spatial context as abstract and sterile. I argue that this time is different: a more sophisticated social physics, spatial analysis and GIScience are emerging that emphasize heterogeneity and spatial context as key drivers of interesting behavior. I also argue that new social physics suggests another path to geographic knowledge somewhere in the middle: mesogeography – a focus on how processes evolve in spatial context. I discuss GIScience techniques and approaches that can facilitate the quest for mesogeographic knowledge.

scholarly journals Urban Analytics: History, Trajectory, and Critique

2021 ◽  
Author(s):  
Geoff Boeing ◽  
Michael Batty ◽  
Shan Jiang ◽  
Lisa Schweitzer
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Spatial Analysis ◽  
Social Control ◽  
Spatial Interaction ◽  
Urban Network ◽  
Marginalized Communities ◽  
Ethical Concerns ◽  
Professional Discipline ◽  
Network Analytics

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

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