名词•[spay-shull day-ta] represe•数据ntative of a specific, geographic location on the surface of the Earth.
Overview
No matter what your interests are or what field you work in, spatial data is always being considered whether you know it or not. Spatial data, also known as地理空间数据，是描述与地球表面上有关特定位置有关的任何数据或包含有关的任何数据的术语。
Spatial data can exist in a variety of formats and contains more than just location specific information. To properly understand and learn more about spatial data, there are a few key terms that will help you become more fluent in the language of spatial data.
Vector
Vector data is best described as graphical representations of the real world. There are three main types of vector data: points, lines, and polygons. Connecting points create lines, and connecting lines that create an enclosed area create polygons. Vectors are best used to present generalizations of objects or features on the Earth’s surface. Vector data and the file format known as shapefiles (.shp) are sometimes used interchangeably since vector data is most often stored in .shp files.
Raster
Raster datais data that is presented in a grid of pixels. Each pixel within a raster has a value, whether it be a colour or unit of measurement, to communicate information about the element in question. Rasters typically refer to imagery. However, in the spatial world, this may specifically refer to orthoimagery which are photos taken from satellites or other aerial devices. Raster data quality varies depending on resolution and your task at hand.
为了识别地球表面上的确切位置coordinate systemis used. Normally, an x and y-axis are used in mathematical systems, but in geography, the axes are referred to as lines of latitude (horizontal lines that run east-west) and longitude (vertical lines that run north-south). Each axis represents the angle at which that line is oriented with respect to the center of the Earth, and so the units are measured in degrees (°)
Georeferencing and geocoding are different but similar processes since both involve fitting data to the appropriate coordinates of the real world. Georeferencing is the process of assigning coordinates to vectors or rasters so they can be oriented accurately on a model of the Earth’s surface. The data used in geocoding are addresses and location descriptors (city, country, etc.). Each of these locations is given the exact coordinates of reference for that location on the surface of the Earth.
处理和分析空间数据的最常见方法是使用GIS，或者，或地理信息系统. These are programs or a combination of programs that work together to help users make sense of their spatial data. This includes management, manipulation and customization, analysis, and creating visual displays. A user will typically use multiple spatial datasets at one time and compare them or combine them with one another. Each spatial dataset may be referred to as a layer.
If you were using GIS for a municipality project, you might have vector data like street data (lines), neighbourhood boundary data (polygons), and high school locations (points). Each dataset would exist as its own layer in your GIS. Placement of layers is important for visual purposes as it will help you understand the various types of data and present your findings in an easily understandable way. In this case, you would want to make sure that high school points and street lines are layers above neighbourhood boundaries. Otherwise, you would not be able to see them.
The field and study of GIS extends much further than digital mapping and cartography. It consists of a variety of categories includingspatial analysis，遥感和地理化。在这些GIS字段中，空间数据变得更加复杂和难以使用。
In addition to raster and vector data, there is alsoLIDAR数据（也称为点云）和3D数据。LiDAR数据是通过卫星，无人机或其他航空设备收集的数据。3D数据是扩展典型的纬度和经度2-D坐标的数据，并将高程和 /或深度纳入数据。虽然复杂，但这些数据充满了信息，可用于解决与地球表面有关的各种问题。
使用图形的空间数据
地图是一个常见的做法展示空间data as they can easily communicate complex topics. They can help validate or provide evidence for decision making, teach others about historical events in an area, or help provide an understanding of natural and human-made phenomena.
When creating visuals, graphics, or maps with spatial data, there are a variety of geographic elements to consider. One of the most important and coincidentally most problematic elements is projection. The projection of a map describes the way that the Earth’s surface, a three-dimensional shape, is flattened and presented on a two-dimensional surface. No projection is perfect and depending on your projection you may be sacrificing accuracy in shape, area, distance, or direction.
Choropleth maps easily communicate differences, consistencies, or patterns across space. Classified areas in a choropleth map will have distinct boundaries whereas heat maps, which demonstrate the concentration or density of a phenomenon, have indistinct boundaries. Classification or heat maps can be used as the bottom layer for other variables like car accidents or crime to highlight certain trends and potential correlations.
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The images above demonstrate a few different ways that spatial data can be displayed. Image a. shows the locations where graffiti has been identified by city custodians in the City of Vancouver. Image b. uses the same point location data, but displays the information as a choropleth map. City region boundaries are highlighted in different colours to describe the density or amount of graffiti taking place within these neighbourhoods. Image c. used the original point location dataset to create a heat map. In this case, city regions are not of interest and rather the spread or pattern of graffiti occurrences throughout the city as a whole is shown.
Using Spatial Data for Statistics
As it is with any data, to truly make sense of spatial data and understand what it is saying you must perform some level of statistical analysis. These processes will help you uncover answers and lead you to make better decisions for your organization. The major difference between spatial data and all other types of data when it comes to statistical analysis is the need to account for factors like elevation, distance, and area in your analytical process.
While needing to account for additional variables about a location may be intimidating, many spatial statistic processes are quite similar to basic statistical methods. For example, interpolation can help you estimate or predict the value of a sample, and spatial interpolation can help you estimate or predict the value of a variable in a sample location. Similarly, spatial autocorrelation measures the degree of similarity between sample locations just like typical autocorrelation is done.
Mapping is also no longer limited to the natural world. Indoor mapping and wayfinding are becoming much more popular especially in large buildings and institutions like malls, arenas, hospitals, and campuses. This field of study is new but shows no signs of stopping. Everyone has a smartphone these days and uses it to help them navigate the natural world, so why not help people navigate the indoors too?
尽管有许多工具和软件可以帮助您使用空间数据，但FME是需要的软件。integrate their spatial data. Safe Software and FME came into existence because of this exact problem. Spatial data varies widely and is often stuck in formats that cannot be easily used by all applications, making it extremely difficult for GIS experts to make use of all the information they have. While it was possible totransformproprietary formats in the past, much of the data would be lost in theconversion. Thus, FME was born.
Raster datais data that is presented in a grid of pixels. Each pixel within a raster has a value, whether it be a colour or unit of measurement, to communicate information about the element in question. Rasters typically refer to imagery. However, in the spatial world, this may specifically refer to orthoimagery which are photos taken from satellites or other aerial devices. Raster data quality varies depending on resolution and your task at hand.
GIS & Location Intelligence
Raster & Aerial Imagery
LIDAR＆POINP云
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C。
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C。
为什么要空间数据？
GIS和BI
Spatial Data On The Web
FMEDesktop
FME云
