GIS basic software and operation (v)

Original GIS basic software and operation (v)

Practice v. Basic operations of spatial analysis

Basic Operations for spatial analysis

Spatial Analysis Module

Spatial analysis is a spatial data analysis technique based on the location and morphology of geographic objects, which is aimed at extracting and transmitting spatial information. Spatial analysis is the main feature of geographic information system. Spatial analysis capability (especially the ability of extracting and transmitting spatial implicit information) is the main aspect of GIS distinction and general information system, and is also a main index to evaluate the success of a geographic information system.

Most of the exercises in this chapter use the spatial analysis extension, and to use the spatial analysis module, first execute the menu command "Custom" > "extension" in ArcMap, and in the Extension management window, tick "spatial
Analyst ". Then, in ArcMap
Right-click the blank area of the toolbar, and in the right-click menu that appears, locate the Spatial
Analyst Item, click the entry to display the Spatial Analysis toolbar in ArcMap.

Perform menu commands < environment >-< work empty on the Geoprocessing toolbar
Set some parameters related to the spatial analysis operation. Here, set a working directory in the current workspace and scratch workspace. Because the process of spatial analysis produces some intermediate results, when the working directory is set, these intermediate results are saved under the specified path.

Understanding Raster Data

In ArcMap, create a new map document, load the raster data: Slope1, right-click the layer Slope1, and view the properties. In the Layer Properties dialog box, click the Source option to view the relevant properties and statistics for this raster layer.

Open the Spatial
Analyst Tools toolbar, click the icon to view a statistical histogram of the raster data:

New ArcMap Map document: Load discrete raster data: Landuse, right-click on the Landuse layer's open attribute table.

View the field "Count" to see the number of grid cells per farm class

Cut raster data with freeform (Extract by Mask)

Practice Content: Extract raster data from any polygon.

In ArcMap, load raster data landuse and vector data clippoly.shp.

In Arctoolbox, double-click the Spatial
Analyst Tools > Extract Analysis > Extract by Mask.

Note the results after the extraction:

Grid computing is the most common method for data processing and analysis in raster spatial analysis, and it is widely used to solve various problems.

In Arctoolbox, double-click the Spatial
Analyst Tools > Map algebra > Raster Calculator.

Construct expression: [Landuse]*[extract_land1]
, perform raster layer Landuse and raster extract_land1 for clipping
The multiplication operation between the. The resulting result is landuse data that is clipped by a freeform polygon.

Raster Reclassification (Raster reclassify)

Practice content: Convert continuous raster data to discrete raster data.

In ArcMap, create a new map document, load the raster data Slope1, and in Arctoolbox, double-click the Spatial
Analyst Tools > Reclassification > Reclassify to open the Reclassify dialog box.

The slope raster is subdivided into 5 categories: 0–8, 8–15, 15–25, 25–35, and more than 35 degrees.

Raster calculations-Query for eligible rasters (Raster Calculator)

Practice content: Find areas with slopes below 25 degrees.

In Arctoolbox, double-click the Spatial
Analyst Tools > Map algebra > Raster Calculator to open the Raster Calculator dialog box.

construct expression [slope1]<=25

The raster assignment that satisfies the criteria is 1, and the remaining rasters are assigned a value of 0

Area tables (tabulate areas)

Practice content: Calculates the area of the cross-tab between two datasets and outputs a table.

Load the Landuse92 raster layer, and in Arctoolbox, double-click the Spatial
Analyst Tools > Area Analysis > Area tab to open the Area tab dialog box.

Specify the partition data and the input raster data as shown.

Open the resulting cross-area data table, view the records, and understand what the meaning of this operation is?

Zonal statistics (Zonal statistic)

Practice content: Summarize the raster data values in another dataset area and output the results as a statistical chart.

Create a new map document in ArcMap, load a raster layer
R5yield (grain-producing area classification map), grid organic (soil organic matter content distribution map).
In R5yield, 5 grain-producing regions are divided according to production:

In Arctoolbox, double-click the Spatial
Analyst Tools > Area Analysis > Show partition Statistics in table to open the show Partition Statistics in table dialog box.

Specify the parameters as shown, confirming that you have the following data table:

Study the above data sheet carefully and understand what this operation means.

Click the drop-down menu in the data table above to execute the "create Diagram" command.

According to the wizard, set parameters to generate a statistical chart of soil organic matter content (average) in different grain-producing regions.

As can be seen from the statistical chart, the lowest yield area has lower organic content, and the content of organic matter is higher in the middle region. This indicates that higher organic matter content will result in higher yields. Low organic matter content in the highest yield area may be the effect of other factors.

Buffer Analysis (buffering)

Buffer analysis is a polygon region of a certain width that is established around a geographic entity in order to identify its effects on surrounding objects. Buffer analysis is an analytical method used to determine the spatial proximity or proximity of different geographic features.

As one of the spatial analysis functions of GIS, the application of buffer analysis is very extensive, and it is often used to analyze the influence of some properties of vector entities on the surrounding. For example, the amount of waste gas emitted by factories in urban areas, the spatial range of traffic noise pollution, the influence of lakes on the surrounding farmland, etc.

Practice content: Select a target feature and build its line buffer.

Step 1: Add the Buffer Wizard to the menu

In the ArcMap window, execute the menu command: Customize > Customize mode, open the Customize dialog box, and switch to the Commands tab. On the Commands tab, select Tools in the Category list box, select the Buffer Wizard in the Commands list box, hold down the left mouse button, and drag it to the existing toolbar.

Step 2: Load the street data and set the map units

Create a new map document, load the street layer Aiostreets and the city cadastral Layer Aiozonecov (map units: meters), and set the units in the data frame properties to "meters".

Step 3: Select the target Street

Execute the menu command: select > select by attribute. To construct an expression in a dialog box: "Str_name"
= ' CYPRESS ' so that a street named CYPRESS is selected from the layer aiostrees.

Step 4: Create a line buffer for the target street

Click the buffer icon to open the Buffer Wizard dialog box:

As shown in the Buffer wizard, a 50-meter buffer (a new polygon layer) is established for the selected street "CYPRESS".

Spatial relationship query (Select by location)

Practice content: On the basis of the previous step, find plots that intersect the 50-meter buffer of the street "CYPRESS".

Execute menu command: select > select by location to open the Select by Location dialog box.

All plots that intersect the 50-meter buffer of the street "CYPRESS" are thus shown.

Spatial interpolation of sampled data (Interpolate)

Spatial interpolation is often used to convert the measurement data of a discrete point into a continuous data surface to be compared with the distribution pattern of other spatial phenomena, which includes two algorithms, spatial interpolation and extrapolation. The spatial interpolation algorithm is used to calculate the data of other unknown points in the same region through the data of known points, and the principle of extrapolation is to deduce the data of other regions through the data of the known region.

Practice content: Through the method of space interpolation, the data of the point is extended to the continuous space, and the temperature distribution map is obtained.

experimental data : Temperature. Two fields in SHP
Y01 and Y02, respectively, recorded the average annual temperature of 16 meteorological observatories in 2001 and 2002. YNBOUNDARY.SHP is a border map of Yunnan province.

Create a new map document, load the layer temperature. SHP and YNBOUNDARY.SHP. In Arctoolbox, double-click the Spatial
Analyst Tools > Interpolation > Spline method, open the Spline Function dialog box and specify the parameters as shown.

Once determined, the following temperature spatial distribution map is obtained (the same effect is obtained by modifying the legend). Similarly, a 2002-year average spatial distribution map can be generated.

2001

2002

Average annual temperature map after interpolation in space (spline function method)

In Arctoolbox, double-click the Spatial
The Analyst tool > interpolation > Inverse distance weighting method opens the Inverse Distance Weighting dialog box, which allows you to re-interpolate the spatial interpolation by setting the relevant options and parameters to create an average annual temperature distribution map after the inverse distance weighting method interpolation:

2001

2002

Average annual temperature map after interpolation in space (inverse distance weighting method)

Grid cell statistics (cell Statistics)

Practice: On the basis of the previous step, the annual average temperature distribution map was obtained according to the average temperatures of 2001 and 2002.

In Arctoolbox, double-click the Spatial
Analyst Tools > Local > Cell statistics, Open the Cell Statistics dialog box and specify the parameters as shown.

2001, 2002 average temperature figure after interpolation in space

Neighborhood Statistics (neighborhood)

Neighborhood analysis, also known as window analysis, is primarily used in raster data models. Geographic features have a certain degree of relevance in space, and for one of the geo-elements described in raster data (i,
J) rasters tend to affect the attribute characteristics of the surrounding raster. It is an important task for computer geo-analysis to accurately and effectively reflect the characteristics of this kind of things ' spatial relations. Window analysis refers to a raster data system in one or more raster points or all data, a fixed analysis of the radius of the analysis window, and in this window, such as extreme values, mean, such as a series of statistical calculations, so that raster data effective horizontal direction extension analysis.

Several types of analysis windows are supported:

In ArcMap, the various operators supported by the neighborhood statistics feature are:

• Majority (Majority)

• Maximum Value (Maximum)

• Mean Value (Mean)

• Median (Median)

• Minimum value (Minimum)

• Minority (Mi

• rity)

• Scope (range)

• Standard deviation (Deviation)

• Total (sum)

• Degree of variability (Variety)

• High throughput (High pass)

• Low throughput (lower pass)

• Focus flow (Focal flow)

Original raster (total sum) neighborhood statistic Raster

Practice content:

Create a new map document in ArcMap, load raster data: Emidalat,
Open the Spatial Analysis toolbar, perform the neighborhood statistics command, and specify the parameters as follows.

In Arctoolbox, double-click the Spatial
Analyst Tools > Neighborhood Analysis > Block statistics, Open the Block Statistics dialog box and specify the parameters as shown.

Will get a raster blockst_emid1 after the operation of a neighborhood operation, which is a 3x3 grid, on Emidalat
The cells in the grid use the "mean" (Mean) operator for the results obtained by the neighborhood operation.

Make the layers blockst_emid1 and Emidalat by setting the legend
As an effect, zoom in on the map and observe the difference between BLOCKST_EMID1 and the original raster by alternately opening and closing the layer blockst_emid1 in the Layer Control Panel.

Original Raster

Grid after neighborhood statistics

Wang Smooth
Comment posted: 2016-01-14 14:56:06
Updated on: 2016-04-28 21:42:08
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GIS basic software and operation (v)