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There is a datum transformation available in Arc. Kertao_To_WGS_1984. When you load both these datasets into ArcMap, is it not giving you a warning re this (different coord sys)? Open up the Coord sys tab of the data frame properties, then click the Transformation button. The pull down should list your available options.
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05-15-2015
04:03 AM
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Why don't you just enable editor tracking? This provides created / edited dates and users automatically. See here : http://resources.arcgis.com/en/help/main/10.2/index.html#//00170000016p000000
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05-11-2015
12:27 AM
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Did you do a full install? It defaults to all extensions I think. Last time I looked, OSM gave you the opportunity to download the data as shapefiles. Then you don't need to convert anything. Maybe OSM is directly readable anyway, see here : http://blogs.esri.com/esri/arcgis/2011/06/06/arcgis-editor-for-openstreetmap/
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05-01-2015
04:55 AM
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Curtis, that is what I did afterwards (Copy Raster), to bring the final bit depth down from 64bit to 32bit. My rasters were in an fgdb. Maybe it is an idea to have an environment setting for the bit depth? I see in your 2nd script you pointed to in your link is exactly the same approach. I am obviously following in the steps of the master...
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04-30-2015
11:13 PM
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If this is the solution to your problem, please mark it as such. Thanks, N
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04-30-2015
11:04 PM
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I ask this because "Generate Near Table" would be a good starting point, with your distance (or a little more) as your max distance. You can put the same point feature as the input and the near features. Then to process this you will probably need some python again...
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04-30-2015
06:59 AM
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What version of Arc do you have, and access level (basic, advanced etc).
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04-30-2015
06:51 AM
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That sounds complicated! There may be cleverer ways but I would probably force this through an iterative loop as a dictionary. Buffer each point, and search for within. This would mean that you would read your 10,000 points, 10,000 times of course. Do you mind me asking why you need to do this?
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04-30-2015
06:46 AM
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Xander, no I suppose this is not a question. Just an interesting method. So, I would if I knew how.... Marked it "Assumed answered" instead, so you got the . Don't see how Cell Statistics could help for this data. I wanted each new raster to be added only if the existing raster is null. And in my priority order. As the iteration loop indicates. I was quite surprised how fast this loop was, each only taking a sec or so. The time consuming bit was the save at the end. Do you have any insight on how or if the bit depth of the output can be controlled? Or even why the environment compression setting doesn't seem to be honoured. My version 10.2.2.
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04-30-2015
06:20 AM
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A few days ago I was building a cost surface (so maybe this belongs in the Spatial Analyst section). The input was an extensive roads network, with different classes of roads (HIGHWAY", "MAJOR ROAD", etc). I separated out each type, and rasterized the vectors, with an "impedance" value. This sort of equates to the speed of the road, or rather the times to pass through each pixel of the raster. So a highway would have less impedance than a street. Then I had to combine these all back together to form my cost surface using con/isnull functions. I was contemplating how to put together a multiple embedded con / isnull when I though I would give this a try. It worked perfectly, and was very fast. The raster size was +16000 by +14000. featList is an ordered in priority list of my road classes. cntFeat = 0
for r in featList:
rName = r[1]
if rName in rasList:
cntFeat += 1
print "Processing {}".format(rName)
if cntFeat == 1:
Ras = Float(arcpy.Raster(rName)) / 100
else:
Ras2 = Float(arcpy.Raster(rName)) / 100
Ras = Con(IsNull(Ras), Ras2, Ras)
Ras = Con(IsNull(Ras), secsPerCellOther, Ras)
Ras.save("Imp_Final") This iterative approach is a good one to remember. btw, the individual inputs were turned into LONG (16bit type) after X 100 just to save some space & time. I could not get feature to raster to output anything other than a 64bit raster if the input variable was a double. Also could not get env.compression to work either.
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04-30-2015
01:20 AM
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Obviously change the paths and field names to reflect your data. And make a backup copy of your manholes first
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04-30-2015
12:59 AM
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Try something like this: import arcpy
# source & target data location
updateTable = "c:/Data/ESRI-SA/ArcForums/MovingPoints.mdb/tblManholeUpdate"
targetFc = "c:/Data/ESRI-SA/ArcForums/MovingPoints.mdb/TM29_WGS84/Manholes"
# fields to be read
updateFields = ["ManholeID", "X", "Y"]
# fields to be updated
fields = ["ManholeID", "SHAPE@XY"]
# load the update info into a dictionary
# ManholeID should be unique in this data
updateDict = {}
with arcpy.da.SearchCursor(updateTable, updateFields) as Cur:
for row in Cur:
updateDict[row[0]] = [row[1], row[2]]
# read the target fc and update
with arcpy.da.UpdateCursor(targetFc, fields) as Cur:
for row in Cur:
if row[0] in updateDict:
xy = updateDict[row[0]]
x = xy[0]
y = xy[1]
row[1] = (x, y)
Cur.updateRow(row)
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04-30-2015
12:55 AM
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Wouldn't saving your layer as a layer file, then re-load it, do what you want to do? As long as all the field names are the same, the symbology should work fine.
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04-30-2015
12:20 AM
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That just looks like FromCoord ToCoord for 2 control points. If we just look at the differences in X & Y for both points : For X : 13460.909 - 12203.844 = 1257.07 2547042.49 - 2545777.15 = 1265.34 For Y : 24402.85 - 23260.347 = 1142.50 7609270.91 - 7608137.67 = 1133.24 So only 8 or 9m difference on either axis. So we might assume that there is no swing (angular difference), between the 2 coordinate systems. So if you are prepared to accept that slight error, you could just do this is your spreadsheet. TargetX = (X - LocalOrigX) + ProjOrigX and similarly for Y. You have 2 control points, so just make an average of the 2.
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04-29-2015
11:24 PM
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