def compute_gwr(cityid):
input_path = path.join(LATLNGS_SHP_DIR, '%s_age.shp' % cityid)
output_path = path.join(CACHE_DIR, 'gwr', '%s.shp' % cityid)
rasters_path = path.join(CACHE_DIR, 'gwr_rasters')
print 'Computing gwr for city %s' % cityid
arcpy.GeographicallyWeightedRegression_stats(input_path, 'income', 'price', output_path,
'ADAPTIVE', 'BANDWIDTH PARAMETER',
'#', '25', '#', rasters_path,)
print 'DONE!'
def gwr_shape(SJfile):
print "Creating GWR layer..."
GWRfile = "gwr_final.shp"
GWRdbf = "gwr_final_supp.dbf"
if os.path.isfile(GWRdbf) == True:
os.remove(GWRdbf)
if arcpy.Exists(GWRfile):
arcpy.Delete_management(GWRfile)
arcpy.GeographicallyWeightedRegression_stats(SJfile, "canrate",
"grid_code", GWRfile, "FIXED",
"AICc")
print "Finished GWR!\n"
bar['value'] = 100
bar.update()
idwbtn.config(state=NORMAL)
tractsbtn.config(state=NORMAL)
# If an error occurred, print line number and error message
import traceback, sys
tb = sys.exc_info()[2]
print "Line %i" % tb.tb_lineno
print e.message
#############################
# Run Regression on the new joined censusjoin shapefile
arcpy.env.overwriteOutput = True
workspace = "C:\project1"
try:
# Set the current workspace (to avoid having to specify the full path to the feature classes each time)
arcpy.env.workspace = workspace
# 911 Calls as a function of {number of businesses, number of rental units,
# number of adults who didn't finish high school}
# Process: Geographically Weighted Regression...
gwr = arcpy.GeographicallyWeightedRegression_stats("censusjoin.shp",
"canrate", "MEAN",
"CancerGWR.shp",
"ADAPTIVE",
"BANDWIDTH PARAMETER")
except:
# If an error occurred when running the tool, print out the error message.
print(arcpy.GetMessages())
print "OLS Complete. PDF Exported."
arcpy.RasterToPoint_conversion(inraster3, rasterPoint3, "VALUE")
arcpy.RasterToPoint_conversion(inraster4, rasterPoint4, "VALUE")
arcpy.RasterToPoint_conversion(inraster5, rasterPoint5, "VALUE")
arcpy.RasterToPoint_conversion(inraster6, rasterPoint6, "VALUE")
# spatial join points to fishnet and get the average values
spatialJoin_result = root + "spaResult_ESI.shp"
SpatialJoinESI(inboundary, rasterPoint1, rasterPoint2, rasterPoint3,
rasterPoint4, rasterPoint5, rasterPoint6,
spatialJoin_result)
# run GWR
GWR_result = root + "GWR_resultESI.shp"
predictor = "pre1;pre2;pre3;pre4;pre5"
arcpy.GeographicallyWeightedRegression_stats(
spatialJoin_result, "DV", predictor, GWR_result, "ADAPTIVE",
"BANDWIDTH PARAMETER", "#", "50", "#", "#", "#", "#", "#", "#")
# reclassify GWR predicted value and get the FUI classification
# Replicate the GWR result
GWR_reclassify = output
arcpy.Copy_management(GWR_result, GWR_reclassify)
arcpy.AddField_management(GWR_reclassify, "ESI", "DOUBLE", 20, 5)
# get the min and max predicted values
GWR_min = GWR_reclassify[:-4] + "_min.shp"
GWR_max = GWR_reclassify[:-4] + "_max.shp"
arcpy.Sort_management(GWR_reclassify, GWR_min,
[["Predicted", "ASCENDING"]])
arcpy.Sort_management(GWR_reclassify, GWR_max,
def GWRFunction(
LocPts, TempPts, RLoc, TTimes, coutdir, ctempdir, InputDir, TempStation,
Datadict, HVal, LVal, TempDir, mth, var, PreName, minstr, Spref, Conv,
ConvEq, ConvUnits, ConvFolder, ConvName
): ######Function to execute GWR, Rasters for English Standar Metrics and converstion to internation metrics if requested
opdt = dt.datetime.utcnow()
arcpy.CopyFeatures_management(LocPts, TempPts)
ptdt("CopyStationsAndPredPoints")
ourFields = arcpy.ListFields(TempPts)
print "in memory points location field names"
for afield in ourFields:
print afield.name
###FIDToXYLocation = {}
####rows = arcpy.da.SearchCursor(LocPts, ["FID", "X", "Y"])
###for row in rows:
#####FIDToXYLocation[row[0]] = [row[1], row[2]]
RasterLst = []
ConvRasterLst = []
KMLLst = []
ConvKMLLst = []
for num, targetTime in enumerate(TTimes):
thisIndex = targetTime[2]
print thisIndex
outputdirectory = coutdir + "\\o" + minstr + "_" + str(num)
if not os.path.exists(outputdirectory):
os.makedirs(outputdirectory)
intermediatedirectory = ctempdir + "\\o" + minstr + "_" + str(num)
if not os.path.exists(intermediatedirectory):
os.makedirs(intermediatedirectory)
print num, targetTime
starttime = targetTime[0]
print starttime
endtime = targetTime[1]
print endtime
modelrunstarttime = dt.datetime.now()
opdt = dt.datetime.utcnow()
arcpy.AddField_management(TempStation, "VALUE" + str(num), "FLOAT")
ptdt("Add VALUE FIELD")
StationsWithInvalidValues = []
opdt = dt.datetime.utcnow()
with arcpy.da.UpdateCursor(TempStation,
["STATION_CO", "VALUE" + str(num)]) as rows:
for row in rows:
if mth == "Sum":
stationvalues = [
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
]
if all(((avalue > LVal and avalue < HVal)
and avalue is not None)
for avalue in stationvalues):
val = sum([
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
])
row[1] = val
rows.updateRow(row)
elif mth == "Max":
stationvalues = [
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
]
if all(((avalue > LVal and avalue < HVal)
and avalue is not None)
for avalue in stationvalues):
val = ([
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
])
Mval = max(val)
row[1] = Mval
rows.updateRow(row)
elif mth == "Min":
stationvalues = [
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
]
if all(((avalue > LVal and avalue < HVal)
and avalue is not None)
for avalue in stationvalues):
val = ([
Datadict[row[0]][atime]
for atime in Datadict[row[0]].keys()
if atime <= endtime
])
Mval = min(val)
row[1] = Mval
rows.updateRow(row)
else:
print stationvalues
print "row contents ", str(row[1])
StationsWithInvalidValues.append(row[0])
del row
ptdt("Add parameter values.")
print "Invalid Value Stations: ", str(len(StationsWithInvalidValues))
cellsize = "0.05"
explanatory_field = "RASTERVALU"
kerneltype = "ADAPTIVE"
bandwidth = "BANDWIDTH_PARAMETER"
dependent = "VALUE" + str(num)
distance = ""
numberofneighbors = 23
out_featureclass = "in_memory\\out" + str(num)
out_featureclass = TempDir + "\\outf" + str(num) + ".shp"
out_prediction_featureclass = "in_memory\\outpp" + str(num)
out_prediction_featureclass = TempDir + "\\outfc" + str(num) + ".shp"
out_prediciton_raster = "in_memory\\outpr" + str(num)
opdt = dt.datetime.utcnow()
arcpy.GeographicallyWeightedRegression_stats(
in_features=TempStation,
dependent_field=dependent,
explanatory_field=explanatory_field,
out_featureclass=out_featureclass,
kernel_type=kerneltype,
bandwidth_method=bandwidth,
distance=distance,
number_of_neighbors=numberofneighbors,
weight_field="",
coefficient_raster_workspace="",
cell_size="",
in_prediction_locations=TempPts,
prediction_explanatory_field=explanatory_field,
out_prediction_featureclass=out_prediction_featureclass)
ptdt("GWR done.")
ptdt(out_prediction_featureclass)
print "shape 1"
ptdt(LocPts)
ptdt(RLoc)
ptdt(intermediatedirectory)
ptdt(outputdirectory)
ptdt(InputDir)
ptdt(thisIndex)
ptdt(var)
ptdt(PreName)
ptdt(minstr)
RasterRes, CRasterRes, KMLRes, CKMRes = CreateRasters(
LocPts, RLoc, num, out_prediction_featureclass,
intermediatedirectory, outputdirectory, InputDir, thisIndex, var,
PreName, minstr, Spref, Conv, ConvEq, ConvUnits, ConvFolder,
ConvName)
RasterLst.append(RasterRes)
ConvRasterLst.append(CRasterRes)
KMLLst.append(KMLRes)
ConvKMLLst.append(CKMRes)
return RasterLst, ConvRasterLst, KMLLst, ConvKMLLst