Browsing by Subject "Spatial"

Now showing 1 - 6 of 6
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    A bio-economic assessment of the spatial dynamics of U.S. corn production and yields
    (2012-02) Beddow, Jason Michael
    This dissertation reports on an investigation into the effects of location on corn production and productivity. The landscape of crop production is dynamic--where crops are produced changes dramatically over time. The answers to important questions about the potential impacts of global climate change and whether agriculture will be able to meet the world's increasing need for food are affected by the moving footprint of production. However, most studies of agricultural productivity and the effects of global warming do not consider that agriculture moves, and that the concomitant changes in natural services have important effects. A full set of county-level census data on corn production and area in the United States have been digitized and assembled for the first time, and new methods have been applied to account for changing geopolitical boundaries. Concepts adapted from economic index number theory are used to show that some 15 to 20 percent of the change in U.S. corn output over the past 130 years has come about due to shifts in where corn is produced. A newly developed, long-run, corn-specific weather dataset is used with the county data to show that, because of changes in the location of production, U.S. corn is now grown in cooler climates than it was a century ago, possibly offsetting some of the potential impacts of climate change. Finally, methods from ecological modeling, spatial econometrics, and crop modeling are combined to create a corn yield model that is then used to develop a location- and season-specific crop suitability indicator that takes into account the intra-seasonal dynamics of weather and the complex relationships between weather and yields. It will be shown that the suitability metric developed in this study gives results that are both consistent and more interpretable than more traditional approaches.
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    Does the right hand know what the left hand is doing? General Assistance welfare crime, and punishment in the United States
    (2013-07) Shannon, Sarah
    Both the welfare state and the criminal justice system have undergone tremendous changes in the past 50 years. While the "right hand" carceral state has swelled through increased populations and spending, the "left hand" welfare state has simultaneously shifted caseloads and spending toward programs that support and reward the working poor and away from cash programs for those in deep poverty. This dissertation examines the theoretical and empirical connections between the changes in these two "hands" of the state using the particular case of General Assistance (GA) welfare programs from 1960 to 2010. In three sets of analysis, this study examines what factors account for major changes in GA policy since the late 1950s, as well as how GA welfare provision has affected state incarceration rates and crime rates at the state and county level over time and space. Results from these analyses highlight two important points: 1) the outlook for low-income men (and others not eligible for federal welfare programs) has become more dire over the last several decades as states have ended income supports for this population in conjunction with higher rates of incarceration; and 2) the loss of such income supports impacts public safety since greater provision of GA is associated with reductions in several types of crime.
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    Enabling Neighborhood Health Research and Protecting Patient Privacy
    (2021-08) Krzyzanowski, Brittany
    Maps and spatial analysis offer a more comprehensive understanding of complex neighborhood health relationships, and yet there is a remarkable lack of maps within the literature on neighborhood health. Review of the literature confirmed that only a small proportion of articles on neighborhood health (28%) contained maps. Despite this, our subsequent survey showed that the majority (63%) of investigators created maps, worked with maps, or used mapping software to explore their data at some point during their study. Neighborhood health investigators are not neglecting to explore the spatial nature of their data, but rather, they are just not publishing the maps that they are using. One of the major barriers identified by our survey was privacy regulations, such as HIPAA law, which stood as a direct barrier for 14% of survey respondents who created maps but did not share them. Many researchers find core elements of the HIPAA privacy provision specific to map data ambiguous or difficult to understand, which is reflected in disagreement and uncertainty in research and policy circles on how to enact this provision. This dissertation provides a thorough examination of the safe harbor provision and elucidates the ambiguity within the law to encourage safe and effective sharing of mapped patient data. Moreover, many scholars and policy makers have challenged this rule, saying that it is possible to share finer-grained mapped health data without jeopardizing patient privacy. One promising strategy is regionalization, or zone design, which offers a way to build finer-grained geographical units in ways that integrate the HIPAA safe harbor requirements. This dissertation explores two existing regionalization methods (Max P Regions and REDCAP) and also introduces two novel variants of these approaches (MSOM and RSOM) which we compare and contrast in terms of fitness for analysis and display of protected health information. Each regionalization procedure has its own strengths and weaknesses, but REDCAP provides the best overall performance. In general, all of the regionalization procedures produced contiguous regions that result in a better resolution map than the current standard for sharing patient data and offer to help investigators work within the bounds of privacy provisions to share maps and spatial data.
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    Essays in Spatial Economics
    (2020-07) Sood, Aradhya
    Chapter 1 examines how land market frictions can hinder the growth of manufacturing firms in developing economies. Land market frictions are the result of increased land fragmentation, poor land records, and restrictive land use policies. Using manufacturing census from India with unique land data, I document that in regions with smaller land parcel size, firms acquire many small parcels slowly over time, expand building with 4% lower probability, and are 22% smaller in size. I build a dynamic structural model that flexibly captures firm land adjustment costs which vary with the size of adjustment and region. I find that land frictions reduce lifetime producer profits by 6.5%. In some regions, firms pay 119% in additional land aggregation costs over and above the dollar value of land. My results are also consistent with the hypothesis that government-affiliated firms face lower land frictions. I find that private firms pay three times more for land aggregation than government-affiliated firms. I use the model to analyze the effects of a proposed government land-pooling policy on producer profits, firm growth, and land misallocation; and to quantify the expected losses to firms from the 2015 eminent domain restrictions. In Chapter 2, William Speagle, Kevin Ehrman-Solberg and I study racial covenants which were clauses in property deeds that prohibited the sale or renting of a property to specific religious and ethnic minorities. This paper studies the effect of racially-restrictive covenants, prevalent during the early-to-mid 20th century, on present-day socioeconomic outcomes such as house prices and racial segregation. Using a newly created geographic data on over 120,000 historical property deeds with information on racial covenant use from Hennepin County, Minnesota, we exploit the unanticipated 1948 Supreme Court ruling that made racially-restrictive covenants unenforceable. We employ a regression discontinuity around the ruling to document the causal and time-persistent effects of racial covenants on present-day socioeconomic geography of Minneapolis. In particular, we document that houses that were covenanted have on average 15% higher present-day house values compared to properties which were not covenanted. We also find a 1% increase in covenanted houses in a census blocks reduces black residents by 14% and reduces black home ownership by 19%.
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    Lithic Analysis and Spatial Patterning at the Bremer Site (21DK06), Dakota County, Minnesota
    (2015-05) Taft, Mara
    The purpose of this study was to conduct a lithic and spatial analysis of the Bremer Site (21DK06), Dakota County, Minnesota in order to better understand how lithic tools and raw materials were curated at the site, what lithic activities took place at the site, what raw materials were present, and if these raw materials were differentially used. Providing answers to these questions will greatly increase our understanding of the Bremer site, its inhabitants, and their role in the region. These questions are addressed by many different analyses. The results of the chipping debris analysis demonstrate the differential use of raw materials by locality and quality at the Bremer site. Locally available Prairie du Chien chert was the primary material used at the site, yet non-local materials had a large presence there, as well, indicating trade of raw materials throughout the region. Additionally, materials were preferentially chosen based on quality and texture. This indicates a non-random selection of materials based on quality for bifacial tool creation. Two distinctive cultural horizons were identified through the vertical stratigraphy of artifacts within Block 1 with observable differentiations in raw material availability and use. These results indicate cultural differences through time represented in the lithic artifacts and an increase in trade and cultural contact over time at the same site. The horizontal artifact distributions and activity areas at the site were identified through a spatial analysis of the site. This analysis also indicated a division of knapping events by raw material type and by artifact type over space. These studies and results increase our knowledge of the inhabitants of the Bremer site, their lifeways and site occupation, and their relationship to the larger region in which they lived.
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    SpatialHadoop: A MapReduce Framework for Big Spatial Data
    (2016-06) Eldawy, Ahmed
    There has been a recent explosion in the amounts of spatial data produced by several devices such as smart phones, satellites, space telescopes, medical devices, among others. This variety of such spatial data makes it widely used across important applications such as brain simulations, identifying cancer clusters, tracking infectious disease, drug addiction, simulating climate changes, and event detection and analysis. While there are several distributed systems that are designed to handle Big Data in general, e.g., Hadoop, Hive, Spark, and Impala, they all fall short in supporting spatial data efficiently. As a result, there are great research efforts in either extending these systems or building new systems to efficiently support Big Spatial Data. In this thesis, we describe SpatialHadoop, a full-fledged system for spatial data which extends Hadoop in its core to efficiently support spatial data. SpatialHadoop is available as an open source software and has been already downloaded around 80,000 times. SpatialHadoop consists of four main layers, namely, language, indexing, query processing, and visualization. In the language layer, SpatialHadoop provides a high level language, termed Pigeon, which provides standard spatial data types and query processing for easy access to non-technical users. The indexing layer provides efficient spatial indexes, such as grid, R-tree, R+-tree, and Quad tree, which organize the data nicely in the distributed file system. The indexes follow a two-level design of one global index that partitions the data across machines, and multiple local indexes that organize records in each machine. The query processing layer encapsulates a set of spatial operations that ship with SpatialHadoop including basic spatial operations, join operations and computational geometry operations. The visualization layer allows users to explore big spatial data by generating images that provide bird’s-eye view on the data. SpatialHadoop is already used as a back bone in several real systems, including SHAHED, a web-based application for interactive exploration of satellite data.