Browsing by Subject "Cluster analysis"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    Evaluation of Buffer Width on Hydrologic Function, Water Quality, and Ecological Integrity of Wetlands
    (Minnesota Department of Transportation, 2011-02) Nieber, John L.; Arika, Caleb; Lenhart, Christian; Titov, Mikhail; Brooks, Kenneth N.
    Human activities including agricultural cultivation, forest harvesting, land development for residential housing, and development for manufacturing and industrial activities can impair the quality of water entering the wetland, thereby detrimentally affecting the natural ecological functions of the wetlands. This can lead to degradation of biota health and biodiversity within the wetland, reduced water quality in the wetland, and increased release of water quality degrading chemicals to receiving waters. Under natural conditions wetlands develop buffer areas that provide some protection from the natural processes occurring on adjacent areas of the landscape. Buffers serve the function of enhancing infiltration of surface runoff generated on adjacent areas, thereby promoting the retention of nutrients in the soil, and retention of sediment suspended in the runoff water, while still allowing runoff water to reach the wetland through subsurface flow routes. To protect wetlands and receiving waters downstream from the wetlands it is important that wetlands in areas disturbed by human activities be provided with sufficient buffer to prevent degradation of wetland biotic integrity as well as degradation of wetland water quality. The question arises, “How much buffer is sufficient?” The objective of this study was to investigate the sufficiency of buffers to protect wetland biotic integrity and water quality, and to evaluate the benefits extended to wildlife by the habit available in wetland buffers. The study was conducted by using a wetland data base available for 64 wetlands in the Twin Cities metro area.
  • Thumbnail Image
    Item
    How do socio-demographics and built environment affect individual accessibility based on activity space? Evidence from Greater Cleveland, Ohio
    (Journal of Transport and Land Use, 2017) Chen, Na; Akar, Gulsah
    Since the early 2000s, accessibility-based planning has been increasingly used to mitigate urban problems (e.g., traffic congestion and spatial mismatch) from a sustainable perspective. In particular, the concept of accessibility has been applied to investigate transport exclusion in many studies. However, few of them shed light on the effects of socio-demographics (e.g., income and gender) and the built environment (e.g., density) on accessibility at the individual level as a measure of transport exclusion. This study measures individual accessibility as the opportunities available per square mile within individual daily activity space for evaluating transport exclusion status based on the Capability Approach. Using data from the 2012 Northeast Ohio Regional Travel Survey and two opportunity sets (land uses and jobs), we calculate individual accessibility and compare them across three income groups. The comparisons report that low-income people are not disadvantaged in our study region. Path models are estimated to examine the relationships between socio-demographics, built environment, trip characteristics, and individual accessibility. We apply K-means cluster analysis to construct seven neighborhood types for the built environment. The results indicate that the effect of income on accessibility varies by opportunity types and living in urbanized neighborhoods increases people’s accessibility after controlling for other characteristics.
  • Thumbnail Image
    Item
    A multi-dimensional multi-level approach to measuring the spatial structure of U.S. metropolitan areas
    (Journal of Transport and Land Use, 2018) Nasri, Arefeh; Zhang, Lei
    For many years, attempts to measure the urban structure and physical form of metropolitan areas have been focused on a limited set of attributes, mostly density and density gradients. However, the complex nature of the urban form requires the consideration of many other dimensions to provide a comprehensive measure that includes all aspects of the urban structure and growth pattern at different hierarchical levels. In this paper, a multi-dimensional method of measuring urban form and development patterns in urban areas of the United States is presented. The methodology presented here develops several variables and indices that contribute to the characterization and quantification of the overall physical form of urban areas at various hierarchical levels. Cluster analysis is performed to group metropolitan areas based on their urban form and land-use pattern. This allows for a better utilization of land-use transportation planning and policy analyses used by planners and researchers. This clustering of urban areas could eventually help policymakers and decision makers in the decision-making process to evaluate land-use transportation policies, identify similar patterns, and understand how similar policies implemented in urban areas with similar urban form structure would result in more efficient and successful planning in the future.
  • Thumbnail Image
    Item
    A multiscale classification of urban morphology
    (Journal of Transport and Land Use, 2016) Schirmer, Patrick M.; Axhausen, Kay W.
    Various studies in the field of urban planning and design have given recommendations for "good urban forms," suggesting that specific spatial characteristics inform the quality of an urban landscape and the way people perceive and behave in them. When modeling spatial behavior in the form of location choice models or hedonic prices, we should reflect these spatial characteristics through the integration of quantitative attributes such as model variables, which is currently only done in a very limited way. The increasing availability of disaggregated geodata enlarges the options to characterize urban morphology in the form of such attributes. The question for the researcher is which attributes are most useful to reflect characteristics of urban morphology and how can they be processed from the given data. In this paper, we want to address this issue and give an overview of quantitative descriptions of urban morphology. We base our work on a data model that is simple enough to allow for reproducibility in any study area. These attributes are classified in multiple scales to reflect different perceptions of urban morphology. In a case study on the canton of Zurich, we furthermore prove how these characteristics allow for the definition of urban typologies at different scales.
  • Thumbnail Image
    Item
    Statistical Analysis of the Soil Chemical Survey Data
    (Minnesota Department of Transportation Research Services Section, 2010-06) Dhar, Sauptik; Cherkassky, Vladimir
    This report describes data-analytic modeling of the Minnesota soil chemical data produced by the 2001 metro soil survey and by the 2003 state-wide survey. The chemical composition of the soil is characterized by the concentration of many metal and non-metal constituents, resulting in high-dimensional data. This high dimensionality and possible unknown (nonlinear) correlations in the data make it difficult to analyze and interpret using standard statistical techniques. This project applies a machine learning technique, called Self Organizing Map (SOM), to present the high-dimensional soil data in a 2D format suitable for human understanding and interpretation. This SOM representation enables analysis of the soil chemical concentration trends within the metro area and in the state of Minnesota. These trends are important for various Minnesota regulatory agencies concerned with the concentration of polluting chemical elements due to both (a) human activities, i.e., different industrial land usage, and (b) natural geological factors, such as the geomorphic codes and provenance of glacial sediments.
  • Thumbnail Image
    Item
    Transfer Behavior and Off-Peak Commutes
    (Center for Transportation Studies, University of Minnesota, 2024-10) Baek, Kwangho; Khani, Alireza
    To improve transit service for off-peak travelers, an essential yet often underrepresented group, and promote social equity, this study examines off-peak transit commutes and transfers, with a focus on the transitway system in the Twin Cities. The research contrasts off-peak and peak travel behaviors using an onboard survey (OBS) from 2016 and automatic fare collection (AFC) data from 2018 to 2023. The initial analysis involved clustering trips from OBS into 16 regional zones and creating origin-destination matrices to explore spatial travel patterns. Key findings include longer peak-time trips (8.51 miles) compared to off-peak trips (5.74 miles) and a higher concentration of non-work trips during off-peak times. The study also reveals that off-peak trips are more dispersed geographically. In the second phase, path choice sets were generated for each respondent from OBS, and logistic regression models were used to analyze preferences for transitway versus bus-only routes. The results indicated a strong preference for transitways, with 60% of passengers opting for them over buses when travel times were equal. Finally, AFC data was integrated with OBS using machine learning techniques to examine long-term trends, including the impact of the COVID-19 pandemic. Post-pandemic data show an increase in off-peak commutes and transit trips with transfers despite an overall decline in transfers. This study provides insights into evolving transit usage behaviors and highlights the importance of the transitway system in facilitating efficient travel.
  • Thumbnail Image
    Item
    Transportation Planning to Support Economic Development: An Exploratory Study of Competitive Industry Clusters and Transportation in Minnesota
    (Center for Transportation Studies, University of Minnesota, 2015-01) Munnich, Lee; Iacono, Michael Jr.; Dworin, Jonathan
    This project seeks to advance the state of knowledge of the relationship between transportation and economic development by investigating how firms in competitive industry clusters use transportation networks and what role the networks play in the formation and growth of these clusters. The approach combines quantitative and qualitative techniques to geographically identify competitive industry clusters and to investigate the role of transportation. The U.S. Cluster Mapping tool is used to identify competitive clusters by employment location quotients in 25 Minnesota metropolitan and micropolitan regions. Twelve competitive clusters were selected for further study, and in-depth interviews and site visits were conducted with businesses in each cluster to explore the competitive importance of different modes of transportation. These methods can yield valuable insights into how transportation functions as an input within competitive industry clusters and how it can inform economic development strategies tailored to certain locations and industries.