On the site for the product it says the following under the GPS Performance section:
- Go to the link above ------> Hit the "support tab" ---------> and the hit "GPS performance" (for some reason, my link doesn't lead directly to the page I was on)
Here's a copy/paste of what it says in that section:
How accurate is MobileMapper CX?
MobileMapper CX provides real-time, sub-meter accuracy through DGPS corrections. The optional MobileMapper Beacon receiver provides beacon corrections to MobileMapper CX via Bluetooth technology.
Note: Sub-meter performance is subject to satellite and environmental conditions. See the MobileMapper CX datasheet for details.
Can I post-process MobileMapper CX data?
Yes. Some data logging applications running on MobileMapper CX record GPS measurements that can be post-processed by MobileMapper Office.
How do I set up the MobileMapper CX for real-time differential correction using SBAS (WAAS/EGNOS/MSAS) signals?
Run the DGPS Configuration program (tap Start>Programs>GPS Utilities>DPGS Configuration > Select Mode > SBAS and tap OK. With the receiver set to None (autonomous mode), Beacon (MobileMapper Beacon) or Other RTCM Source, the MobileMapper CX will not use SBAS corrections even if it receives them.
Where do I have to be in order to use SBAS corrections?
You should use SBAS signals only if you are in North America or the Northern Pacific (WAAS), Europe (EGNOS) or Japan (MSAS). Although you may pick up SBAS signals outside of these areas, the corrections are calculated using ground stations only in these areas. The farther away from these areas your receiver is, the more error you may be introducing to your position calculations.
This information should be in your maunual .... no??
There's also information on integrating with arcpad at this link
Basically, you can do the extract using ogr2ogr as long as you give the Census tract ID, so it's really an issue of getting 72,000 ogr2ogr calls.
ogr2ogr -where "tract = '<tract_id>'" /dest_folder /source_folder block_shapefile -nln block_shapefile_<tract_id>
Notes:
- You don't have to specify the source format, ogr2ogr will figure it out. You specified shapefile, so that's what I'm assuming.
- If you are not changing the format, you also don't have to specify the destination format. Otherwise, to get a shapefile, add `-f "ESRI Shapefile"
- I'm using the
-where switch to subset the data. Remember that attribute query is much faster than spatial query. Your question was a little vague, so I don't know if you intend to join the blocks to the tracts by attribute or spatially, but I highly recommend the former.
- Note that I am assuming your blocks shapefile has a tract ID column in it. Most Census data sets will have a hierarchy of ID columns, i.e. the blocks shapefile will probably have a state, county, and tract, as well as block ID column, but may not have a column concatenating them all together. They must be concatenated, because counties are only unique with states, tracts are only unique with counties, and blocks are only unique within tracts. So you will have to either (a) create a new field with state & county & tract as your ID field, or add all three criteria to the where clause.
So how do you build 72,000 ogr2ogr calls programmatically? You can use any tool you want, but here's an example with R:
library(foreign)
dfBlocks = read.dbf("/source_folder/shapefile_name.dbf", as.is=TRUE)
strTract = unique(dfBlocks$tract_id)
for (i in 1:length(strTract)) {
strOGR = paste(
"ogr2ogr -where \"tract_id = '", strTract,
"'\" /dest_folder /source_folder layer_name -nln base_name_",
strTract, sep=""
)
system(strOGR)
}
You could also collect the system calls in one step, then iterate it to run the ogr2ogr call in a separate loop, or at a later time, or write it to a bash script that you run from the command line.
The major disadvantage is that it will scan the data source once for each ogr2ogr call, so actually importing the source data, iterating, and writing a shapefile for each row, would probably be more efficient. But I would recommend trying it in something other than R, which is somewhat slow at reading large spatial datasets (I ran an import just of the counties of the US, ~3000, and after 15 minutes I cancelled the import.)
Best Answer
I would recommend using the open source GIS Whitebox GAT (http://www.uoguelph.ca/~hydrogeo/Whitebox/) for creating seamless mosaics from aerial photography. Please note that John Lindsay is the lead developer of Whitebox GAT (according to his bio).
Here's a possible workflow:
If you have multiple colour air photos, split them into their RGB components using the Split Colour Composite tool. You will want to mosaic each band separately then create a colour composite mosaic at the end.
You may want to use the Correct Vignetting tool (to be released in version 3.0.6) to remove the gradual darkening towards the image corners that commonly occurs with air photos. This will greatly improve the mosaic quality.
Use the 'Find Tie Points' tool (to be released in version 3.0.6) to automatically find tie points between adjacent images in the group of images. Notice that you don't have to do this for each RGB band, but rather only use one (e.g. the red band images).
Use the Image Rectification tool to register adjacent images.
Use the Mosaic With Feathering tool to join adjacent images. This may have to be done several times as you build up the images, and you'll have to do it for each of the Red, Green and Blue bands. Importantly, this tool will join the images such that the boundaries between them are not obvious in any way. There will be gradual gradients from one image to the next and it will also perform histogram matching to match the radiometric properties of each image in the mosaic.
Create a colour composite mosaic by using the Create Colour Composite tool, combining the Red, Green, and Blue mosaics together.
If the colour quality is not as good as you would like, I'd recommend using the Balance Colour Enhancement tool to improve it. This works quite well.
I don't have a colour air photo example, but here is a seamless greyscale mosaic from air photos using this workflow. Notice that it has the vector footprints of the original three air photos: