Transform seismic spatial data into thematic maps

Author: Flyingis
You are welcome to repost this article, but please note the author and original article links,Prohibited for commercial purposes!

Using the method described in parse seismic space data on the USGS website page, the seismic space data has been obtained and displayed on the map hierarchically, we can intuitively understand the earthquake distribution and earthquake size in California. Next, we need to use mathematical methods to further analyze and process the data to make it a more professional geographic map.
First, the hot spot analysis (Hotspots) is performed on the seismic spatial information ). The earth source distributed on the map has a very high magnitude, but the earthquakes in adjacent areas are very weak and have little impact. Some have a high magnitude, and the earthquakes in adjacent areas are equally strong, the impact is high. Using the symbols rendered in different levels to display all earthquake information does not clearly reflect such a rule. Relatively speaking, administrators need to pay more attention to the latter because Earthquakes often occur in these areas, in addition, the magnitude is large, causing more serious harm. Hot Spot analysis requires some mathematical calculations. The basic formula is as follows:

Using yesterday's solution to the problem, we used the popular analysis tools provided on the ArcMap platform to directly call the corresponding interfaces in the Python environment integrated with ArcMap to achieve dynamic analysis and plotting.

Arcpy. HotSpots_stats ("quakes", "magnqueue", "hsa110kmagnqueue", "Fixed Distance Band", "Euclidean Distance", "None", "110000 ")

 

 
Quakes. shp is the seismic data layer that has been generated before. magn.pdf is the Magnitude field added in the previous python script. HSA110KMagnitude is the output result element class, followed by the parameter settings involved in calculation. The HSA110KMagnitude attribute field contains the GiZScore field. A larger value indicates that the magnitude of the earthquake in the surrounding area of the point is about large. You can perform further Z-value rendering on the value to achieve more obvious results.

Arcpy. ZRenderer_stats ("hsa110kmagnscores", "GiZScore", "ZRenderer. lyr ")

The result shows the size distribution of all hot spots with warm and warm tones. Note that the dot rendering symbol here is totally different from that of quakes. shp. ArcGIS Spatial Analysis and 3D analysis have always been the most important GIS analysis technology. Next we will use Python for further processing. Taking spatial interpolation analysis as an example, since the GiZcore field exists, everything is very easy.

Arcpy. env. extent = "California"
Arcpy. Idw_3d ("ZRenderer. lyr", "GiZScore", "cntidw", 3000, 2, "FIXED 750000 ")
Extract = arcpy. sa. ExtractByMask ("cntidw", "California. shp ")

 

ZRenderer. lyr is the input, GizScore is the field used for interpolation, cntidw is the output, followed by the interpolation parameter settings, and the output result is cropped Based on the California region as the boundary, this completes the entire calculation.
Through the above three different spatial analyses, we can see that the integration of the Python environment can easily perform various spatial data processing and spatial analysis and computing on the GIS platform, with more obvious effects and more flexible operations, on this basis, you can add map names, legends, scales, and north-point information to Output Maps of different types of topics. All operations are completed in ArcMap 9.4 beta1, and 9.3 can also run python scripts. However, after you run the computation in an external python development environment, manually add the generated results to the desktop platform or application.