A big data approach to mitigating the MAUP in measuring excess commuting

Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking

A big data approach to modelling urban population density functions: from monocentricity to polycentricity

Urban studies have a long tradition of examining the regularity of urban structure by modelling urban population density functions and probing the theoretical or behavioural foundation behind it. Previous studies commonly used census data in areal units such as census tracts or census block groups, which varied a great deal in area size and shape and led to the zonal and scale effects, commonly referred to as the modifiable areal unit problem (MAUP). This study uses big data of individual vehicle trips in Tampa, Florida, to define the precise population and employment distribution locations, and then aggregates them with uniform areal units such as squares, triangles, and hexagons to examine and mitigate the scale and zonal effects. Both monocentric and polycentric models are employed in the analysis of urban population density functions. The results suggest that the exponential density function remains the best fitting monocentric function in most areal units including census units and designed uniform units. The polycentric model reveals two centres (downtown and University of South Florida) exerting influences on the areawide population density pattern. The zonal effect is not significant in the designed uniform units, but the scale effect remains evident in all areal units

Modeling Urban Population Density Functions and Excess Commuting: A Big Data Approach to the Modifiable Areal Unit Problem

Urban studies have long focused on modeling population density functions and analyzing the theoretical and behavioral foundations of urban structures. Simultaneously, understanding excess commuting, which highlights inefficiencies in urban transportation, is crucial for urban planning and transportation analysis. This dissertation bridges these perspectives by leveraging big data from individual vehicle trips in Tampa, Florida, to explore the relationship between population and employment distribution patterns and measure excess commuting with greater precision. By addressing the modifiable areal unit problem (MAUP), this study refines the understanding of population and employment distributions. It aggregates data into uniform areal units and thus effectively examines scale and zonal effects. This approach reveals that the exponential density function best fits the population density pattern across various areal units, including census units and designed uniform units such as squares, triangles, and hexagons. While the zonal effect is not significant across uniform units, the scale effect remains evident across all areal units. Utilizing a combination of Linear Programming (LP) and Integer Linear Programming (ILP) approaches, this research compares minimum and actual commuting patterns to examine excess commuting. The analysis at both the census tract and block group levels, as well as at the individual trip level, demonstrates that smaller spatial units yield shorter minimum commuting times and distances, and highlights the scale effect of MAUP. At the census tract level, the average minimum commuting time and distance are 5.67 minutes and 3.04 kilometers, respectively while at the block group level, these values are 5.05 minutes and 2.68 kilometers. The ILP analysis at the individual trip level shows a significantly lower minimum commuting in time or distance, namely 3.6 minutes and 2.27 kilometers, respectively. This dissertation employs detailed big data to offer a comprehensive and accurate assessment of urban population density functions and excess commuting. The findings emphasize the importance of using individual data in urban transportation analysis to mitigate the MAUP, provide refined insights into urban structure and transportation inefficiencies, and ultimately contribute to more effective urban planning and policymaking

Examining potential impacts of external drivers on environmental instream flows on the Cahaba River, Alabama

Electronic Thesis or DissertationWater is the key to the lives of all living creatures. Human beings need to ensure there will be safe and abundant water resources in the future. The important issue of water is not only about the water quality, but also about the water quantity. As the most effective indicator of water quantity, instream flow is critical for us to understand the water quantity. It includes the variations of water flows, representing the high and low periods of the river flow. The low flow of rivers is important to preserve the harmony of the environment physically, biologically, and socially. Jowett (1997) pointed out that the minimum flows in rivers and streams provide protection at a certain extent for the aquatic environment. In addition to the creatures that are living within the water, human beings also need water. However, with external drivers, such as climate change, population growth, and riparian policy relaxation, the low period of the water flows in the Cahaba River can be even lower in the future. Hence, this is what this research addresses and why it is critical. This research examines the effects of increasing human activities, such as irrigation and municipal water uses, and climate changes on instream flows in the Cahaba River, Alabama, and indicates what kind of actions might be implemented to prevent negative consequences. The goal of the result of this research is to estimate the possibility that the Cahaba River will be unable to meet the increasing needs of water uses due to the external changes. Based on the results of the scenarios designed in this study, with the external factors, such as climate change, population growth, and riparian policy relaxation, the instream flow of the Cahaba River can be at risk in the future, and we should start to take actions for preventing the trend

Assessment of flow–ecology relationships for environmental flow standards: a synthesis focused on the southeast USA

Environmental flow standards are a management tool that can help to protect the ecosystem services sustained by rivers. Although environmental flow requirements can be assessed using a variety of methods, most of these methods require establishing relationships between flow and habitat of species of concern. Here, we conducted a synthesis of past flow–ecology studies in the southeast USA. For each state or interstate river basin, we used the published data to determine the flow metrics that resulted in the greatest changes in ecological metrics, and the ecological metrics that were most sensitive to hydrologic alteration. The flow metrics that were most important in preserving ecological metrics were high-flow duration and frequency, 3-day maximum and minimum, and number of reversals. The ecological metrics most sensitive to hydrologic alteration were mostly related to presence or absence of key indicator species.</p