Exploratory Use of Raster Images as a Data Source for Agricultural Commodity transportation Modeling

摘要

In the area of freight planning, particularly in commodity modeling, it is often a daunting task to identify data sources, especially data disaggregate to county or less than county levels. If one considers the case of agricultural commodities, information availability is further reduced since agricultural census data collection is infrequent and is not complete in terms of crops covered, nor does data exist below the county level in terms of geographical aggregation. Additionally, data sources such as the Freight Analysis Framework (1), FAF, often present annual data. Since transportation models are usually developed for peak periods and/or typical days, the traditional assumption of flat peak factors are not valid for agricultural commodities that are seasonal and that have seasonal patterns with inter-state and intra-state variations. CropScape is a tool from the United States Department of Agriculture (USDA) that provides complete coverage for the 48 contiguous states and reports all crops with a spatial resolution of less than one acre. It is in this setting that we tested the use of CropScape in two different analyses: FAF disaggregation and seasonality analysis. We present results that include models for FAF disaggregation that greatly outperform the best models currently available in the literature and a procedure for computing agricultural seasonality for any desired geographical aggregation.