CyberGIS and spatial data science
CyberGIS and spatial data science
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DOI：
10.1007/s10708-016-9740-0
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发表时间：
2016-12-01
期刊：
影响因子：
2.7
通讯作者：
Wang, Shaowen
中科院分区：
文献类型：
--
作者：
Wang, Shaowen
在当今地理空间互联的世界中，区域和城市分析对于理解耦合的环境和人类系统变得越来越重要。这些系统的复杂性及其在不同时空尺度上的连通性，对有效解决各种区域问题和城市可持续发展提出了严峻的挑战。然而，应对此类挑战的传统科学方法往往在空间和时间上是支离破碎的，并且由于无法利用空间大数据而受到限制，这使得对大型和多个时空尺度的连通性进行推断变得困难或不可行。迫切需要重大科学突破和技术创新来发现和理解人与地方之间复杂且动态的空间联系。迫切需要结合丰富而复杂的空间数据、分析和模型的跨学科方法来激发变革性的地理空间创新和发现，从而有效、及时地解决具有挑战性的区域和城市问题。通常嵌入地理参考的空间数据对于许多科学领域（例如，生态学、地理和空间科学、地球科学和社会科学等）都很重要，并且也有利于解决许多区域和城市问题（例如，城市绿色基础设施和可持续性）。然而，近年来，随着日益复杂的基于位置的传感器和设备（例如社交网络、智能手机和环境传感器）的广泛部署和使用，此类数据已激增至海量和显着的复杂性。从众多来源收集的空间大数据被广泛用于以前所未有的规模来检测我们的自然、人类和社会系统，同时提供了巨大的机会来动态洞察复杂现象。然而，获取、分析和综合各种空间数据——各种科学解决问题实践的基础过程——已经变得越来越困难，并且不那么容易实现。
In today’s geospatially connected world, regional and urban analysis has become increasingly essential to understand coupled environmental and human systems. The complexities of such systems and their connectivity at various spatial and temporal scales have posed daunting challenges to effective solutions to a variety of regional problems and urban sustainable development. Conventional scientific approaches to such challenges, however, tend to be fragmented in space and time and constrained by the inability to take advantage of spatial big data, which make extrapolation over the connectedness across large and multiple spatial and temporal scales difficult or infeasible. Major scientific breakthroughs and technological innovations are urgently needed to discover and understand complex and dynamic spatial connections between people and places. Interdisciplinary approaches combining rich and complex spatial data, analysis and models are highly demanded to ignite transformative geospatial innovation and discovery for enabling effective and timely solutions to challenging regional and urban problems. Spatial data often embedded with geographical references are important to numerous scientific domains (eg, ecology, geography and spatial sciences, geosciences, and social sciences, to name just a few), and also beneficial to solving many regional and urban problems (eg, urban green infrastructure and sustainability). In recent years, however, this type of data has exploded to massive volume and significant complexity as increasingly sophisticated location-based sensors and devices (eg, social networks, smart phones, and environmental sensors) are widely deployed and used. Spatial big data collected from numerous sources are extensively employed to instrument our natural, human and social systems at unprecedented scales while providing tremendous opportunities to gain dynamic insight into complex phenomena. However, to access, analyze, and synthesize various spatial data—a foundational process of various scientific problem-solving practices—has become increasingly difficult and is not