arcGIS python两个点要素类，分别求核密度，求取栅格的平均值，并进行栅格计算，栅格裁剪Clip。
信息熵（区位熵）计算公式：
#-*- coding:UTF-8 -*-
import arcpy
from arcpy import env
from arcpy.sa import *
env.workspace = r"C:UsersHPDesktop20210323tweets.gdb"
#CheckOut liscen SpatialAnalysis
arcpy.CheckOutExtension("ImageAnalyst")
arcpy.CheckOutExtension("Spatial")
env.overwriteOutput = 1
#（1）核密度计算
outKDens = KernelDensity("DACA", "NONE", 11265.60, 250000, "SQUARE_KILOMETERS")
outDACA = r"C:UsersHPDesktop20210323tweets.gdbtemp_DACA_KDE"
outKDens.save(outDACA)
print("---DACA KDE Successful-----")
outKDens2 = KernelDensity("mudslide", "NONE", 11265.60, 250000, "SQUARE_KILOMETERS")
outMudslide = r"C:UsersHPDesktop20210323tweets.gdbtemp_mudslide_KDE"
outKDens2.save(outMudslide)
print("---mudslide KDE Successful-----")
#read Raster
DACA_Raster = arcpy.Raster(outDACA)
Mudslide_Raster = arcpy.Raster(outMudslide)
z = 0.000001
#（2）栅格计算器，求平均值，求Log2(A/B/average(A/B))
average_KDE = r"C:UsersHPDesktop20210323tweets.gdbtemp_average"
average_Raster = (DACA_Raster + z)/(Mudslide_Raster + z)
average_Raster.save(average_KDE)
meanValueInfo = arcpy.GetRasterProperties_management(average_Raster, 'MEAN')
meanValue = float(meanValueInfo.getOutput(0))
print("average Value is:" + str(meanValue))
resultMap = Log2(((DACA_Raster + z)/(Mudslide_Raster + z))* 1/meanValue)
result_KDE = r"C:UsersHPDesktop20210323tweets.gdbresult_KDE"
resultMap.save(result_KDE)
print("---output result KDE Successful-----")
#（3）栅格裁剪Clip
#Clip Raster
clip_shp = r"C:UsersHPDesktop20210323tweets.gdbstates_lower48"
clip_KDE = r"C:UsersHPDesktop20210323tweets.gdbclip_KDE"
arcpy.Clip_management(result_KDE, "#", clip_KDE, clip_shp, "0", "ClippingGeometry")
print("---output clip KDE Successful-----")
