My work
Deleting unimportant fields from missed tabular join datasets
3 problems
1. If a point is on boundary will it be double counted?
2. More than one points in a boundary
3. No points within a boundary
Results 725 out of 749 selected
Exported selected features as sdatt_ele_w_ccd_type07is1
Switch selection to see what’s not joined
Cleaning CCD data
Now
Dissolving sdatt by level to see what has matched
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Matching SDNAME with CCD LEANM07 field.
Add more steps here
About datasets
CCD200708MN_Level07 : Indicates subselection of CCD200708MN data by just Level07 = 1 ie
elementary
CCD200708MN_Level07 : Indicates subselection of CCD200708MN data by just Level07 = 2 ie middle
CCD200708MN_Level07 : Indicates subselection of CCD200708MN data by just Level07 = 3 ie high
CCD200708MN_Level07 : Indicates subselection of CCD200708MN data by just Magnet07 = 1 ie magnet
schools
CCD200708MN_Level1 : Indicates subselection of CCD200708MN data by just Level07 = 1 ie elementary
But joining ccd_level1 data with sdatt dissolved on elem
Did 2 joins one with CCD points as the target layer (CCD200708MN_level1) and sdatt08_copy_elem as
the join layer and another with the sdatt08_copy_elem as the target layer and CCD points as the join
layer. Overlaid ccd point join on the sdatt layer
Check the table also
Table shows polygons with more than 1 join as selection. So these need to be looked at individually so
that exact CCD points that need to be assigned can be identified.
Now doing joins for the middle schools
Following image shows polygon with 2 or more points, does not include polygons with no points
Now view of the table
Now let’s do the high schools
High school join results
Overlaid ccd join on sdatt join
Now address all those areas in sdatt where joincounts are 0 or more than 1
Join count 1
Table join
LEAID = ‘ ‘
Overview table
Tabular join Spatial Join (based on Join count)
L1 L2 L3 L1 L2 L3
Total sdatt 749 405 388 749 405 388
Joined 510 151 277 447 154 193
Not joined 239 254 111 30 183 26
Joined > 1 272 68 79