Jenny Palomino ELP 2015 July 16, 2015 Getting Started with GIS - - PowerPoint PPT Presentation
Introduction to Geographic Information Systems/Science (GIS) Jenny Palomino ELP 2015 July 16, 2015 Getting Started with GIS Fundamentals of Geospatial Analysis What is Remote Sensing? Useful (Free!) Sources of Geospatial Data Software
Fundamentals of Geospatial Analysis What is Remote Sensing? Useful (Free!) Sources of Geospatial Data Software Options for Working with Geospatial Data Easy (Free!) Web-based Options for Sharing your Geospatial Data Explore Web Applications
pattern spatial data
location distance neighborhood event
network scale accuracy space time spectra text
domain
change persistence clustering heterogeneity classification connections proximity context coincidence
core concepts questions
Where are our key stakeholders located? Where is the best place for a new facility? Who owns land around national parks and how are they using those lands? How might the range of a species change in the future?
Maggi Kelly, UC Berkeley
Primary Data Types vector: point, line, polygon raster: continuous (e.g. elevation) or discrete surfaces (e.g. land use) Common Data Formats vector: shapefile, database geometry, tables (.dbf, .xlsx), KML, GeoJSON raster: ASCII, GeoTIFF, JPEG2000, MrSID, IMG, GRID, HDF5
Image: Maggi Kelly, UC Berkeley
Is the observed spatial pattern due to more than just random process? Two Measures of Spatial Autocorrelation:
across a region
b.values ~0.0: no spatial autocorrelation
within the region Example in presentation is based on Moran’s I Global and Local Indicators (other indicators detailed here).
http://www.pysal.org/users/tutorials/autocorrelation.html Image: ESRI help
Clustered with Global Moran's I = 0.24 Three Hot-spots/Clusters
Active and Passive Remote Sensing
e.g. Landsat
is collected... e.g. lidar
Maggi Kelly, UC Berkeley
Global Land Cover 2000 Project Maggi Kelly, UC Berkeley
NDVI = (Near Infrared Band -Red Band) (Near Infrared Band + Red Band) Values range from -1.0 to 1.0: water ~ -1.0 barren area ~ 0.0 shrub/grass ~ 0.2-0.4 forest ~ 1.0
http://en.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index
GeoTIFF of Landsat 8 image for the San Francisco Bay Area NDVI result from ~ -1.0 (red/orange) to ~1.0 (green = vegetation)
Admin Boundaries: US Global Elevation: US Global Land Cover: US Global Protected Areas: US Global Climate: US Global
http://gif.berkeley.edu/resources/data.html
Proprietary: ESRI ArcDesktop, MapInfo, IDRISI (low cost), Manifold (low cost) Open Source Desktop: QGIS, Grass, uDig, SAGA, gvSIG, GMT, R Spatial Packages Web-based: MapGuide (open source), CartoDB (limited free edition) Mobile: Amigo Cloud, Open Data Kit (open source), QGIS for Android (open source)
Proprietary: ENVI, ERDAS Imagine, PCI Geomatica, IDRISI, ESRI ArcDesktop Open Source Remote Sensing Tools: Orfeo Toolbox, Grass, R, SAGA, QGIS, Opticks, OSSIM, ILWIS, Python packages (Rasterio, scikit-learn, scikit-image ) Free (but not open source) Remote Sensing Tools: – Object-based image analysis: Spring – Web-based: Google Earth Engine (currently free to Trusted Testers)
https://earthengine.google.org/#intro
Steiniger and Hunter, 2013. Computers, Environment and Urban Systems, 39: 136-150
D3 Visualization R tools for web and sharing: R Shiny and R Markdown NoSQL Spatial Databases: MongoDB, Google Cloud Datastore and Google Cloud BigTable (both cloud-based) Open Data Kit IDEs for Python: IPython Notebook, PyCharm Web-based GIS: MapGuide, CartoDB (limited free edition) Python Spatial: GeoPandas, shapely Python Image Processing: Rasterio, scikit-learn, scikit-image R Spatial: sp, raster, R Spatial: rgeos, rgdal GDAL/OGR in Python
CartoDB (limited free edition): example 1 example 2 Google Maps API and Fusion Tables ArcGIS Online and ArcGIS Open Data (both open to non-ESRI license holders)
http://cal-adapt.org/
http://vtm.berkeley.edu/#/data/
http://landcarbon.org/categories/
How might spatial patterns play a role in your area of interest? What kinds of spatial analysis would be useful to explore spatial patterns in your area of interest? Based on the web applications you explored, what ideas do you have for sharing datasets and maps?
http://iep.berkeley.edu/spatialdatascience
jpalomino@berkeley.edu