Quality Control of Spatial Data

I have received a very important dataset of Forest Management Unit (KPH) of South Kalimantan. It has been requested by Sustainable Development of the Wood-Based Industries in South Kalimantan Project (itto-pd397) to Forestry Department. There are many digital versions of KPH boundary. Therefore ITTO-PD397 considered it was very important to clarify which data is used to the forest boundary authorities, Forestry Department.

As one of the project experts, I was forwarded that spatial data in shapefile format. Have a look on it, I came to a clear conclusion; the data can not be used and does not pass spatial data quality control. Here are the reasons why I consider the data is a garbage:

  • There are 13 overlapping polygons;
  • There are 5 voids polygons;
  • There are 3 slipper polygons;

No matter what GIS software are you using. There must be quality control tools in it. ArcView, the most likely used by Forestry Department agencies, has many quality control extension in it. The example for ArcView GIS is CLU Quality Control Extension. ArcGIS has more advance topological check obviously.

This is a message to GIS practitioners who are dealing with data analysis/distribution; “Check your spatial data quality before you distribute it“. I consider Forestry Department, has low capabilities in managing its spatial data quality. This could be one of clear sign that GIS human resources development in Forestry Department is focused on operator level, not on analyst level. GIS is not low level work. Echelon 3 and 4 at spatial-related institution need to know GIS.

So, if next time there is GIS training/course, echelon 3 and 4 need to participated.

.itto-pd397

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Schlagworte: clu, control, data, distribusi, distribution, gis, kph, kphl, kphp, quality, south kalimantan, spatial,