首先,来聊一聊什么是“spatial data”。简单来说,与地理空间信息相关的数据称为空间数据。更具体的,根据Cressie (1993)所述,空间数据可以分为地质数据(geostatistical data),晶格数据(lattice data),和点模式 (point patterns)。其中地质数据包括海拔等数据;晶格数据是指从城市等地区收集的社会经济数据和像素级的卫星...
Spatial data miningis the process of discovering hidden patterns in large spatial datasets. It is a key driver of GIS application development, since it allows users to extract valuable data on contiguous regions, such as distance and direction, and investigate spatial patterns for further analysis, ...
What is spatial data infrastructure ?Manager, Data
Learn about database built-in support for spatial Fig. 1. The different layers and types of complex geospatial data Fundamentals of Geospatial Data ‘Geospatial data’ refers to information about features, objects, and classes on Earth’s surface or even in space. Geospatial data is typically ...
Paper prepared for presentation at the Spring 1989 Symposium on Spatial Statistics, Past, Present and Future, Department of Geography, Syracuse University. 展开 关键词: CiteSeerX citations What is Special About Spatial Data? Alternative Perspectives on Spatial Data Analysis Luc Anselin ...
Data Management Identity & Access Governance Storage Margaret Rouse Technology expert Margaret is an award-winning writer and educator known for her ability to explain complex technical topics to a non-technical business audience. Over the past twenty years, her IT definitions have been published by ...
Spatial Data Science is a subset of Data Science that focuses on the special characteristics of spatial data, using modeling to know where & why things happen.
What is spatial computing? The concept of “spatial computing” is widely attributed to researcher Simon Greenwold, who coined the term in a 2003paper. Greenwold defines spatial computing as the “human interaction with a machine in which the machine retains and manipulates referents to real obje...
Spatial data science is a subset of data science. It’s wheredata science intersects with GISwith a key focus on geospatial data and new computing techniques. Location matters in data science using statistical computing to access, manipulate, explore, and visualize data. ...
Geospatial data typically involves large sets of spatial data gleaned from many diverse sources in varying formats and can include information such as census data, satellite imagery, weather data, cell phone data, drawn images and social media data. Geospatial data is most useful when it can be...