Freight Model Development Quarterly Update (March 2012)

Exploratory Use of Raster Images for Freight ModelingPedro Camargo PhD StudentUniversity of California, Irvine

TRB SHRP2 SYMPOSIUMInnovations in Freight Demand Modeling and Data ImprovementOctober 20131

1OutlineCalifornia Statewide Freight Forecasting ModelCropScapeFAF DisaggregationAgriculture Seasonality AnalysisOther public GIS datasets

22CSFFMCommodity basedDeveloped using FAF3Aggregated to 15 commodity groupsAll agricultural products are in the same group97 Freight Analysis zones in CaliforniaUS network (only major truck routes outside CA)Will be integrated with California Statewide Travel demand Model (CSTDM) passenger travel modelSpatial disaggregation neededTemporal disaggregation needed3Sponsored by CALTRANS: http://www.dot.ca.gov/hq/tsip/otfa/csffm/index.html

3CSFFM4

% of total flows moved on each day Carried over to mode split and assignmentOutput from generation/ distributionWhy do we need a seasonality analysis for CSFFM?4CropScapeProject of the United States Department of Agriculture*Raster images for all 48 contiguous states from 2008 onwards (since 1997 for some states)

* - http://nassgeodata.gmu.edu/CropScape/5

Raster image (From Wikipedia): In computer graphics, a raster graphics image, or bitmap, is a dot matrix data structure representing a generally rectangular grid of pixels, or points of color, viewable via a monitor, paper, or other display medium

GeoTIFF (from Wikipedia): GeoTIFF is a public domain metadata standard which allows georeferencing information to be embedded within a TIFF file. The potential additional information includes map projection, coordinate systems, ellipsoids, datums, and everything else necessary to establish the exact spatial reference for the file

For more references: http://trac.osgeo.org/geotiff/

5General characteristicsDeveloped using remote sensing technologyEach pixel is 0.77 acreVirtually any spatial aggregation can be used for modeling purposesAccuracy:Generally, the large area row crops have produced accuracies ranging from mid 80% to mid 90%105 different crops identified5 types of developed areas and 23 other area types6CropScape FAQ: http://www.nass.usda.gov/research/Cropland/sarsfaqs2.html

6Obtaining an imageAll areas viewed by remote sensing every 5 daysEach pixel in each picture is classified according to its reflectance (256 levels of gray in the picture)Ground truth is checked and used to correlate classes to actual soil use (different crops, barren land, water, populated areas, etc.)The annual picture is created in consideration of all the classifications for each pixel in each image

7CropScape Timeline8CropScape ProcessingCropScape presents processed state AND county data for 2010-12Images for 2012 sum up to about 13.8GbProcessing with free software is painlessGDAL tools inside GRASS*Scripting with Python is trivialProcessing of all images for 2012 on a current high powered laptop takes less than 3h

* - http://grass.osgeo.org/9GDAL - (Geospatial Data Abstraction Library): It is a translator library for raster geospatial data formats

GRASS and Quantum GIS are open source GIS software that are easily automated with the use of Python, have a large and active users base that provided free and efficient support and include most tools found in commercial GIS software9Processing example10

Basically, summing all the pixels for each class in each area

10FAF DisaggregationFAF* is one of the primary sources for data used to develop freight modelsFrequent topic in the literatureApplications in Florida, New Jersey, Washington DC, etc.Lack of variables for less than county geographiesAgriculture is one of the industries with the least amount of available data

* - http://www.ops.fhwa.dot.gov/freight/freight_analysis/faf/ 11References for FAF disaggregation efforts in other states:

New Jersey: http://trid.trb.org/view.aspx?id=847612Florida: http://trb.metapress.com/content/11m412344j8u4651/DC region: http://trid.trb.org/view.aspx?id=1092988Alabama: http://www.uahcmer.com/wp-content/uploads/2010/11/Using-FAF2-Port-Data-to-Model-Freight-in-a-Medium-Sized-Port-City.docx.pdfTexas: http://trb.metapress.com/content/g01q227j554544u0/

11Disaggregating Agricultural ProductsFAF3 baseline is 2007, but CropScape is only available starting in 2008Disaggregation of 2011 provisional FAF with 2011 CropScapeDisaggregation of FAF 3 with 2008 CropScape dataThere is a LOT more that can be tried30 different grain crops75 non-grain crops

12Disaggregating Agricultural Products13

Disaggregating Agricultural Products14

More to exploreSeveral commodity groups could be disaggregated with CropScape:Construction related commodities could be disaggregated by using developed area informationLogs for paper/construction can be disaggregated using areas classified as forests

15Agricultural forecastsCropScape allows for the estimation of currently unused land:Provides an upper limit on planted areasAllied to estimates of future yields, it is a robust forecasting toolExpansion of planted area can be trackedPrecipitation raster images are also available16Seasonality AnalysisWhy compute seasonality factors?FAF presents yearly flows and agriculture is highly seasonalIs it possible to use other data sources?Yes, but only CropScape covers ALL crops and areas consistently

1717Seasonality Analysis18

1 NOTE THAT THIS APPLIES FOR EVERY SINGLE AREA BEING ANALYZE18Seasonality Analysis19

1 More than good data, it allows to create impacting results, like this video2 Note that in some regions winter is relevant (Imperial County), and for others it is really Summer/Fall that accounts for most of the production19VegScapeInformation on vegetation vigor/strengthJust launched in April 2013Much coarser resolution than CropScape (~15 acre)Daily/Weekly information

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Map for 2nd half of July/201020Other available GIS databasesUSDA Natural Resources Conservation Service*Topography / Elevation (raster)Conservation Easements (vector)National Land Cover Data Set (NLCD) (raster)Hydrography (vector)Annual average temperatures (vector)Precipitation (vector)

* - http://datagateway.nrcs.usda.gov/GDGOrder.aspx?order=QuickState21Yearly demand

Crop Areas

Yields

Harvesting periods

CropScape