Latest Contributions by VinceE

Can't replicate. Can you be more specific about the tool you are using or what the issue is? I'm on Pro 3.3.1. Can't comment on ArcMap. Here are two domains, one numeric, one text. Here they are applied to a FeatureClass: Exporting to Excel: Export shows the descriptions, not the codes. This doesn't work for you?   ... View more

Sure, happy to help you work through anything further if you have additional questions! ... View more

For posterity, this seems like a much more efficient option (working with Pandas Spatial Data Frames, instead of Cursors and SelectionByLocation/Attribute): def new_method(pnt: Union[str, os.PathLike], # full path to points box: Union[str, os.PathLike], # full path to boxes box_id: str, # field name containing Box ID sample_count: int, # how many points per box? output: Union[str, os.PathLike] # full path to output FC ) -> None: # function has no return """SpatJoin points with boxes. Convert to SDF. Groups points by joined BOX ID. Select 3 points per group. Exports sampled Spatial Data Frame back to FC.""" pnt_box_join = arcpy.analysis.SpatialJoin(pnt, box, r"memory\pnt_box_join").getOutput(0) # Convert FC to SDF. Drop unused columns. GroupBy BOX_ID column. # Without replacing, sample X# rows from each group. Export SDF back to FC. sdf = pd.DataFrame.spatial.from_featureclass(pnt_box_join) sdf = sdf.drop(columns=["Join_Count", "TARGET_FID", "Shape_Length", "Shape_Area"]) sampled_sdf = sdf.groupby(box_id, group_keys=False).sample(n=sample_count) sampled_sdf.spatial.to_featureclass(location=output, sanitize_columns=False) print("SAMPLED SDF -> FC EXPORTED")     ... View more

Here's an attempt. Hexagons have "populations" between 1 and 5 (light to dark). Black points are the child care centers and their capacities are shown in the adjacent black labels. Regions made up of hexagons that have aggregated enough "population" to reach each capacity for each center are shown in the colors, with the "actual" population gathered labeled with the callouts. The caveats: Regions can overlap if centers are too close (see red/blue). Lots of fixes for this, depending on what you're after. Childcare centers can be "over" capacity. See green region, where the max capacity is 80, and 81 have been "gathered" from adjacent hexagons. Also lots of fixes, depending on needs. Code below, in simple terms: for each center, add the closest hexagon to a region, followed by the next closest, etc., until enough hexagons have been added so the summed population reaches the capacity of the child care center. This runs extremely quickly with ~1,300 hexagons, but is definitely not the most optimized. You'll have to edit this code if you want to use it with your own feature classes. Happy to provide more details if you need them. import arcpy import math import os import sys arcpy.env.workspace = r"YOUR_GEODATABASE_PATH" arcpy.env.overwriteOutput = True # Names of hexagon bins and the associated points. hex_bin = "HexBin" hex_pnt = "HexAggPoint" # Create an info dictionary about the hexagons # {oid: {HEX_GEOM: geometry, POP: integer}} with arcpy.da.SearchCursor(hex_bin, ["OID@", "SHAPE@", "POP"]) as scurs: hex_pop_dict = {oid: {"HEX_GEOM": geom, "POP": pop} for oid, geom, pop in scurs} # Empty dictionary to store info about our output regions. clusters = {} # Loop over points, logging assembled region information in 'clusters' dict. with arcpy.da.SearchCursor(hex_pnt, ["OID@", "SHAPE@XY", "CAPACITY"]) as scurs: for oid, centroid, capacity in scurs: # Get list of (hex_id, distance_to_point) tuples. distances = [] for hex_id, hex_record in hex_pop_dict.items(): hex_centroid_coor = hex_record["HEX_GEOM"].centroid dist = math.dist(centroid, (hex_centroid_coor.X, hex_centroid_coor.Y)) distances.append((hex_id, dist), ) # Sort tuples ascending distance from the point in question. distances.sort(key=lambda x: x[1]) # Loop distance tuples, using the hex ID to accumulate population per hex. # Log the hex IDs that are used. Stop adding more hexes onces the capacity # of the point is reached. gathered_hexes = [] accumulated_pop = 0 for hex_id, _ in distances: gathered_hexes.append(hex_id) accumulated_pop += hex_pop_dict[hex_id]["POP"] if accumulated_pop >= capacity: break # For this Point ID, associated a list of hexes that will create region. clusters[oid] = gathered_hexes print(f"HEX ID: {oid}\nCAPACITY: {capacity}" f"\nACTUAL ACCUM: {accumulated_pop}" f"\nHEX IDS: {gathered_hexes}\n") # CREATE OUTPUT FC, ADD FIELDS region_fc = arcpy.management.CreateFeatureclass(out_path=arcpy.env.workspace, out_name="HEX_REGION", geometry_type="POLYGON", spatial_reference=2232) for fld_name in ["POINT_ID", "HEX_ID", "POP"]: arcpy.management.AddField(region_fc, fld_name, "SHORT") # WRITE OUTPUT RECORDS with arcpy.da.InsertCursor(region_fc, ["SHAPE@", "POINT_ID", "HEX_ID", "POP"]) as icurs: # For this point and list of hexes that should create the region... for point_id, hex_id_list in clusters.items(): # ...and for each hex in the list... for hex_id in hex_id_list: # Using hex's ID, insert Geometry and Population, # plus the ID of both the parent point and the hex. icurs.insertRow([hex_pop_dict[hex_id]["HEX_GEOM"], point_id, hex_id, hex_pop_dict[hex_id]["POP"]]) # Dissolve the hex polygons based on the associated point. Sum the pop of the region. dissolve_fc_path = os.path.join(arcpy.env.workspace, "HEX_REGION_DISSOLVE") arcpy.management.Dissolve(in_features="HEX_REGION", out_feature_class=dissolve_fc_path, dissolve_field="POINT_ID", statistics_fields=[["POP", "SUM"], ])   ... View more

I would explore using Generate Rectangles Along Lines or Strip Map Index Features: Generate Rectangles Along Lines (Data Management)—ArcGIS Pro | Documentation Strip Map Index Features (Cartography)—ArcGIS Pro | Documentation   ... View more

Not entirely sure I follow, but have you looked into Attribute Rules (specifically, "Calculation"-style rules)? They can do a lot of automatic calculations for you in your tables. https://pro.arcgis.com/en/pro-app/latest/help/data/geodatabases/overview/an-overview-of-attribute-rules.htm  ... View more

Can you clarify this? Where is "1234" coming from out of "Site1_UAV_042018"? What parts of the "Name" field would you like to calculate over to the "Site_ID" field? It's not really clear what you're asking. ... View more

Is this a one-time calculation? Or does this need to be dynamic, as your table undergoes changes? What would happen if two rows got deleted, for some reason? So BY0001 and BY0002 were removed (accidentally maybe?), how would those IDs be assigned if those rows were added back in? Is there any reasons that once an ID is assigned, it must remain as that same ID? Or can these IDs be recalculated any time changes are made to the table, so that it is ensured that they are always unique? I think if this is a one-time Field Calculate, this is straightforward, but perhaps less straightforward if this table will be changing a lot for some reason, and all those IDs need to be managed/updated on the fly. ... View more

I think @BarryNorthey is right. See below, where my third point, OBJECTID 7, does not show because the whole field is just spaces (so it is labeling, but it's unseen because it's spaces!). An alternative to the suggestion above using Python would be to improve your label expression to TRIM white space. // Use Trim() to reduce a field full of spaces down to "". DefaultValue(Trim($feature.Label_Main), $feature.Label_Backup) And now here is what we have, because the "Label_Main" values is reduced from "       " down to "", and therefore is considered Empty, and then defaults to the Label_Backup.     ... View more

Can you clarify this a bit, perhaps with a diagram or picture? Do you want to dynamically count the number of points that fall within a given polygon? Or simply count the number of points that have the same "id" as a polygon? I think both Polygons and Points have an "id" field, but where is the "No" field? In the Polygon feature class? ... View more

Arcade uses "==" as it is written to test for null specifically. IsEmpty() is used frequently as well, but that will also return true for empty strings. ... View more