Everything is somewhere...

...That somewhere matters.

Mission

To encourage and support the use of geospatial technologies in research and teaching at Stanford University.

(whatever that means at any given time)

Instruction
Infrastructure
Consulting

Instruction

Weekly Introductory "Spatial data science" Workshops
Vendor Workshops
Guest Lecturing
Course Support
Events

Treat spatial technology awareness as information literacy and research skills

Fill the gap between students who want credit courses and researchers who requires a few "data carpentry" skills

Infrastructure

SGC Computer Lab
Software licensing, provision & support
Cloud-based resources:
- ArcGIS Online
- CARTO
- Mapbox.com
- SimplyAnalytics.com
locator.stanford.edu
earthworks.stanford.edu

Consulting

Outbreak Responder
Nepal Typhoid Survey
Kindred London
Landscape Testimonials
Nomadic Pastoralists

earthworks.stanford.edu

spatial data is different

it has geometry
and scale
and extent
and temporality

It's difficult to find

It's difficult to evaluate

It's difficult to access

Earthworks.stanford.edu

GeoBlacklight
OpenGeoMetadata
GeoCombine

GeoMonitor
Stanford Digital Repository
Stanford Spatial Data Infrastructure

The Stanford Digital Repository treats our spatial data holdings as durable assets.

The Stanford Spatial Data Infrastructure (SSDI) exposes the live OGC spatial data services that power Earthworks fuctionality
(WFS, WMS, etc...)

http://umap.openstreetmap.fr/en/map/air-monitoring-stations-california-2001-2003_41503#7/37.195/-119.773

Earthworks is Geoblacklight
and
Geoblacklight is a Community

http://geoblacklight.org/

GeoBlacklight Contributors

Big Ten Academic Alliance
MIT Libraries
New York University Libraries
Princeton University Library
Stanford University Libraries
University of Minnesota Libraries

https://github.com/OpenGeoMetadata

https://github.com/OpenGeoMetadata/GeoCombine

Princeton 28,872
Stanford 13,962
Harvard 10,448
MIT 7,166
NYU 3,502
Columbia 3,265
Tufts 3,092
UVa 1,256
Michigan 802
Wisconsin 750

OpenGeoMetadata Contributors

Minnesota 624
MassGIS 599
Berkeley 351
Iowa 343
Indiana 291
Purdue 235
Illinois 129
Baruch CUNY 126
Michigan State 43
Maryland 32

https://github.com/geoblacklight/geo_monitor

Geoblacklight is getting better, all the time...

Finding Nomadic Pastoralists from Space

https://link.springer.com/article/10.1186/s41018-018-0030-y

Hannah:

"I can see the settlements in Google Earth, but I know that those images are old and those villages are long gone, but is there a way to get satellite imagery that good, and do a complete settlement survey within a few weeks of the image being taken?"

https://tasks.hotosm.org/

http://foundation.digitalglobe.com/

#Import modules
import glob, os, gdal, fnmatch

#Set base directory to location of script
base_directory = os.path.abspath('')

#Make a list of Multispectral directories with 'MUL' in the path
multi_directory_list = glob.glob(os.path.join(base_directory, '*MUL'))

#Make Panchromatic directory [pan_directory] path by replacing 'MUL' with 'PAN'
for multi_directory in multi_directory_list:
    pan_directory = multi_directory.replace('MUL','PAN')
    print(multi_directory)
    print(pan_directory)
    #Make a list of TIF files in the multi_directory
    multi_tiffs_list = fnmatch.filter((os.listdir(multi_directory)), '*.TIF')
    for multi_tiff_name in multi_tiffs_list:
        print(multi_tiff_name)
#Get the matching panchromatic image for the current multispectral by searching for the RowCol
        multi_rowcol_wildcard = '*'+multi_tiff_name[19:24]+'*'
        #print(multi_rowcol)
        pan_tiffs_list = fnmatch.filter((os.listdir(pan_directory)), '*.TIF')
        pan_tiff_name = fnmatch.filter(pan_tiffs_list, multi_rowcol_wildcard)
        #print(pan_tiff_name[0])
#Pan-sharpen the multispectral using it's corresponding panchromatic image using JPEG compression and internal tiles
        print('pansharpen')
        os.system('python gdal_pansharpen.py -co NBITS=12 -co COMPRESS=JPEG -co JPEG_QUALITY=50 -co PHOTOMETRIC=YCBCR -co TILED=YES ' + os.path.join(pan_directory,pan_tiff_name[0]) + ' ' + os.path.join(multi_directory,multi_tiff_name) + ',band=7 ' + os.path.join(multi_directory,multi_tiff_name) + ',band=5 ' + os.path.join(multi_directory,multi_tiff_name) + ',band=3 ' + base_directory + '/sharp_' + multi_tiff_name)
#Add overviews for resulting tiffs
        os.system('gdaladdo --config COMPRESS_OVERVIEW JPEG --config PHOTOMETRIC_OVERVIEW YCBCR --config INTERLEAVE_OVERVIEW PIXEL -r average ' + base_directory + '/sharp_' + multi_tiff_name + ' 2 4 8 16')
#Create a Virtual Raster Mosaic of VRT type from all resulting sharpened TIFF images
print('create VRT mosaic from SHARP images')
os.system('gdalbuildvrt sharpmosaic.vrt *.TIF')
#Haven't finished figuring out how to get a decently stretched image out of this part. More to do!
print('MBtiles out')
print('gdal_translate sharpmosaic.vrt sharpmosaic.mbtiles -of MBTILES')
print('gdaladdo -r average sharpmosaic.mbtiles 2 4 8 16')

https://github.com/mapninja/DGF_parser

HIVE

13440 x 5400 x .53m pixels
=
20 sq km

Typical Monitor

1440 x 900 x .53m pixels
=
.7 sq km

Or,...
250 shift+arrow pans vs. 7000+

~ 6 hrs

Outcomes

5,000+ sq km
225 settlements
only 2 abandoned by arrival

347 surveys of women of reproductive age
Measurements and histories for 824 of their children <15 years

gis.stanford.edu
github.com/stanfordgeospatialcenter

@mapninja

stacemaples@stanford.edu