Modify urban landuse

Hi,

I have urban landuse data (low, high intensity, Industrial/commercial) in a csv file, e.g.
urban landuse, latitude, longitude

I need to convert it to geogrid binary format. Is there any utility available for this conversion?

thanks,
Mukul

Mukul,
We don't have utility for this specific format. You may follow the instruction here to transfer the data:
http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.9/users_guide_chap3.html#_Writing_Static_Data

Are the values at regularly spaced intervals on a latitude-longitude grid or on a specific map project? If not, interpolating scattered point data onto a regular grid suitable for ingest by the geogrid program may add some complication.

The urban land use is at a spatial resolution of 90 m × 90 m.

On which map projection are the data provided? The geogrid program supports input datasets on polar stereographic, Lambert conformal, Mercator, cylindrical equidistant, and Albers equal area projections. If you data are provided on a different projection, some interpolation would likely be needed.

Just to add to my previous reply,

The data values are at regularly spaced intervals (i.e., the center of each 90x90m pixel) and the projection is NAD 1983 Albers.

Since the geogrid program does support input datasets on an Albers equal area projection with a NAD 83 datum, it looks like there woulndn't be any need to re-project the data. Are the points in the CSV files given in any particular order (e.g., from west to east, and from south to north), or are they in essentially random order? If they are given, e.g., row by row from south to north, then it should be possible to just read successive lines from the CSV file into successive pixel locations in an array. However, if the points are in random order (or something generally unpredictable), then more work would be needed to read the CSV values and place them in the correct array location.

The points start at the northwest then proceed to the northeast. Then the next row (to the south) is started and it goes from west to east.

Is every pixel provided in the CSV file (i.e., do we have missing values for non-urban pixels), or are only urban pixels given?

Each pixel is provided with a value (urban or non-urban). There are no missing values.

So there are categories which are urban and non-urban, but for use in WRF/Urban, only urban categories are of interest.