Introduction to Geographic Information Systems/Science (GIS)beahrselp.berkeley.edu/wp-content/uploads/Jenny-Palomino-Presentation.pdf · Introduction to Geographic Information Systems/Science

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 Options for Working with Geospatial Data Easy (Free!) Web-based Options for Sharing your Geospatial Data Explore Web Applications

Fundamentals of Geospatial Analysis

pattern spatial data

location

distance

neighborhood

event

object

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

What is Spatial Data?

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

Exploring Spatial Patterns

Is the observed spatial pattern due to more than just random process?

Two Measures of Spatial Autocorrelation: 1. Global - quantifies clustering/dispersion

across a region a. values ~ 1.0: highly clustered b. values ~0.0: no spatial autocorrelation c. values ~ -1.0: highly dispersed

1. Local - identifies clusters (hot-spots)

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

Example of Spatial Autocorrelation Results

Clustered with Global Moran's I = 0.24

Three Hot-spots/Clusters

What is Remote Sensing?

Active and Passive Remote Sensing

• Passive = reflected solar light… e.g. Landsat

• Active = an emitted energy source is collected... e.g. lidar

Maggi Kelly, UC Berkeley

Land Cover Classification

Global Land Cover 2000 Project Maggi Kelly, UC Berkeley

Normalized Difference Vegetation Index (NDVI)

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

Example of NDVI Results

GeoTIFF of Landsat 8 image for the San Francisco Bay Area

NDVI result from ~ -1.0 (red/orange) to ~1.0 (green = vegetation)

Useful Sources of (Free!) Geospatial Data

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

Software for Working with Geospatial Data (GIS)

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)

ArcMap Demonstration – Exploring the 10 mile buffer surrounding

National Parks in California

Software for Working with Geospatial Data (Remote Sensing)

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)

Google Earth Engine

https://earthengine.google.org/#intro

Google Earth Engine

Free & Open Source Software in 2012

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

Free & Open Source Software in 2015

Easy (Free!) Web-based Options for Sharing Geospatial Data

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)

Explore Web Applications

http://cal-adapt.org/

Explore Web Applications

http://vtm.berkeley.edu/#/data/

Explore Web Applications

http://landcarbon.org/categories/

Discussion

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?

Spatial Data Science Bootcamp at UC Berkeley

http://iep.berkeley.edu/spatialdatascience

Questions?

[email protected]