Examining Transportation Roadblocks to Community Colleges: Pathways to Educational and Economic Opportunity

This dissertation examines how transportation barriers impact community college (CC) students' ability to achieve their academic objectives. Although CCs serve as critical pathways to educational and economic opportunities for marginalized populations, transportation accessibility remains an overlooked variable in student success. The transition to a CC often signifies a meaningful achievement for a broad segment of the population. For some, securing adequate funding and initiating formal application procedures can seem like insurmountable hurdles. CCs, also known as two-year colleges or junior colleges, are a critical component of the U.S. higher education system. CCs offer an extensive range of courses that lead to associate degrees, certificates, or self-paced learning options, often allowing students to earn credits transferable to four-year institutions for bachelor's or advanced degrees (Kerr & Wood, 2022). CCs are critical to enhancing equitable access to higher education for first-generation college students, individuals from groups historically excluded from higher education, older learners, and workers seeking to enhance their skills (Community College Research Center [CCRC], 2021). An overarching goal of my dissertation is to bring awareness to the transportation challenges faced by CC students when accessing campus and to highlight the need for additional work in this area. There is some research that looks at how certain data points such as socioeconomic status, race, and access to a car are linked to CC access, but there is a lack of research on how access and associated barriers to it affects student completion, including reliable and affordable automobility, public transit, and other modes.This abstract describes the research objectives and overarching methodology and highlights key results from these contributions and concludes with a summary.These ongoing barriers remain prevalent among students in rural, suburban, and economically disadvantaged areas and can become the most significant determining factor in their academic outcomes. This research employs three complementary approaches: (1) a comprehensive literature review of transportation accessibility in higher education contexts, (2) spatial analysis comparing CC accessibility in Texas and California, and (3) case study analysis of hypothetical student experiences at Austin Community College.Chapter 1 - Obstacles and Opportunities for Improving Transportation Access to Community College EducationThe goal of Chapter 1 is to better comprehend the transportation access barriers faced by CCs and develop effective strategies to address them. I conducted an in-depth analysis of 88 studies examining transportation-related issues within higher education institutions. My findings revealed that while previous research has delved into the transportation challenges encountered by communities at universities, it has largely neglected the specific transportation issues faced by CC students. While some research exists in the context of CCs, there is a significant gap in empirical analyses that explore the modal preferences of CC students, examine the correlation between transportation barriers and the academic success of CC students, and evaluate the effectiveness of strategies aimed at improving access to CC campuses.To investigate past work relevant to college access, I performed a systematic literature review on transportation to higher education campuses. Since the existing body of work focusing on transportation access to colleges is limited, a broader search scope was employed to analyze enough studies. From these studies, relevant insights about college access were extracted. Key articles were identified using a hub-and-spoke search process. The analysis section aims to achieve three main objectives: 1) provide summaries of the 88 studies included in the literature review, 2) identify significant themes in the past work on transportation to higher education, and 3) explain why these themes are crucial for studying transportation access to colleges.Shaheen et al. (2017) introduced a framework called the STEPS to Transportation Equity Framework, which serves as a tool for analyzing the various transportation barriers that individuals may encounter during their travels. The STEPS framework revealed that long commute distances, conflicting transit and course schedules, and housing unaffordability can hinder transportation access to CCs. The literature underscores the use of public transit pass programs to assist CC students with transportation, but there's an opportunity to explore innovative approaches to meet the growing access requirements of CC students.Building on these insights from the literature review, Chapter 2 moves from theoretical understanding to geographic analysis by examining two major community college systems.Chapter 2 - A Tale of Two States: Exploring Transportation Accessibility to Community College Education in California and TexasI delve into the extent to which students must travel to reach their nearest CC campus, and the variety of degree and educational programs offered at each CC campus. To achieve this, I selected CCs in Texas and California as case studies to explore how access to education is influenced by the distinct policy models prevalent in these two states.This investigation is driven by a desire to comprehend how transportation access to the two largest CC networks in the U.S., Texas and California, which collectively encompass the two largest states in the country, impacts pathways to employment and transfer routes to universities. These two states play a significant role in shaping the economic, political, and social landscape of the United States.To explore accessibility to CCs, I selected a method of measuring access to CC campuses. Since education is a pathway to employment opportunities and other benefits, the variety of degree and certificate offerings provided by a CC is a measure of the opportunities available. To measure the number of offerings available at CCs, I utilize data maintained by the Integrated Postsecondary Education Data System (IPEDS). I also examine the number of opportunities available at CC campuses that are accessible to individuals in their vicinity.To gain insights into the characteristics of individuals with better access to CCs and those with limited access, I measure access proxy variables alongside three socioeconomic variables collected from the 2021 five-year American Community Survey (ACS) census tract estimates.To investigate our research objectives, I selected statistical tools that enable us to draw inferences from the data used to measure transportation access to pathways to employment and education through CCs. The method I utilized to assess sociodemographic affluence influences access to CCs and their educational offerings is crosstab analysis. Crosstab analysis involves organizing all data points measured into bins, or crosstabs, based on the magnitude of the variable used to define the crosstabs.When it comes to access to CC campuses and education, economic affluence plays a more significant role in California, while educational affluence holds a more prominent position in Texas. In contrast, mobility affluence has a minimal impact on access in both states. On average, educational offerings at the nearest CC campus tend to be higher in Texas than in California. This disparity can be attributed to the differences between California's centralized CC policy model and Texas' decentralized CC policy model. While one state may not clearly offer better CC campus access compared to the other, a comprehensive analysis of access provides a broad understanding of CC access in the two states. However, further exploration should be conducted through more detailed access case studies that are situated in both states.While Chapter 2 provides a macro-level analysis of accessibility patterns across two states, Chapter 3 zooms in to examine the lived experiences of individual students navigating these transportation systems.Chapter 3 - A Case Study Analysis of Transportation Accessibility to Community College on Public TransitThis study delves into the accessibility of public transit to CCs through a comprehensive case study analysis. The primary objectives are to:1) Understand the impact of transportation accessibility challenges on the lives of CC students.2) Show that transportation accessibility should be recognized as a significant barrier hindering CC students' ability to achieve their academic goals.3) Develop a framework to comprehend how equity factors influence student access to CCs via public transit.To address the real-world transportation accessibility challenges faced by CC students, I developed a qualitative research methodology based on hypothetical student profiles. This approach allowed us to gain a deeper understanding of their transportation burdens by presenting realistic barriers and challenges in a definable and descriptive manner. In the fall of 2019, StudentMoveTO, a Toronto survey, conducted the largest survey ever conducted to better understand student travel patterns, experiences, and preferences. The StudentMoveTO survey served as a proxy and framework for my approach to this methodology. Based on these assumptions, I analyzed the data and demographic characteristics of 200 students from the Toronto survey to create 10 hypothetical student profiles that served as a proxy for conducting our research with Austin Community College (ACC) students. For example, my analysis and coding of the student demographic characteristics in the sample revealed approximately 14 significant and repetitive elements (race, age, gender, income, enrollment status, employment, major parental status, transit time, frequency of travel, multiple campus transit, etc.) that correlated with transportation accessibility. Using this framework, I obtained publicly accessible data from ACC and selected a representative sample of approximately 200 students. I then followed the same methodology process as the Toronto survey to calculate and identify 10 hypothetical student personas/profiles that were most likely to matriculate at ACC for the purpose of this research.After presenting qualitative evidence of the transportation accessibility challenges faced by each student profile, I drew some clear observations, themes, and takeaways that advance this discourse. I categorized these as the "Seven Top Barriers of Transportation Accessibility for CC Students." The findings from these three chapters collectively illuminate critical aspects of transportation accessibility for community college students. Chapter 1 established the theoretical framework and identified gaps in existing literature. Chapter 2 provided insights on how different state policy models relate to transportation accessibility and access to educational opportunities. Chapter 3 provided qualitative insights into how transportation barriers manifest in students' daily lives. Together, these findings contribute to our understanding of transportation accessibility in several important ways, as outlined below.This dissertation demonstrates that (1) transportation barriers significantly impact CC student persistence and success; (2) accessibility varies based on geographic location, economic status, and institutional policy models; and (3) addressing transportation equity is essential if CCs are to fulfill their mission of providing inclusive educational opportunities. These findings suggest that integrated transportation planning should be a core component of CC strategic initiatives.In this research, I contribute to both transportation equity and higher education accessibility scholarship by establishing a clear connection between transportation barriers and educational outcomes at CCs, providing a methodological framework for assessing CC accessibility across different policy contexts, and identifying specific intervention points for improving student success through enhanced transportation access.Future research should incorporate direct student surveys, real-time transportation data, and expanded geographic coverage.ReferencesCommunity College Research Center. (2021). An Introduction to Community Colleges and Their Students. Teachers College, Columbia University. [https://ccrc.tc.columbia.edu/publications/introduction-community-colleges-students.html]{.underline}Kerr, E., & Wood, S. (2022, November 29). Everything You Need to Know About Community Colleges: FAQ. [https://www.usnews.com/education/community-colleges/articles/frequently-asked-questions-community-college]{.underline}Shaheen, S., Bell, C., Cohen, A., Yelchuru, B., & Booz Allen Hamilton, Inc. (2017). Travel Behavior: Shared Mobility and Transportation Equity (PL-18-007). [https://rosap.ntl.bts.gov/view/dot/63186]{.underline}Shaw, K., Asher, L., & Murphy, S. (2023). Mapping Community College Finance Systems to Develop Equitable and Effective Finance Policy. Community College Research Center. [https://ccrc.tc.columbia.edu/publications/mapping-community-college-finance-systems-develop-equitable-effective-policy.html]{.underline

Alterations in biochemical profiles of patients with severe COVID-19 pneumonia: Analysis of repeated laboratory tests

Objective: This study was initiated to show the changes in the biochemical profile and identify the mortality risk factors of patients with severe coronavirus disease-19 (COVID-19) pneumonia. Materials and Methods: This study was designed as non-interventional and cohort research. Demographic and clinical data were retrospectively obtained from paper-based documents and electronic health records. Complete blood counts, inflammatory markers, liver, and kidney function tests, and coagulation profiles were recorded 3 times. Two-way ANOVA for repeated measures was used to analyze for continuous dependent variables. Binary logistic regression analysis was performed to determine in-hospital mortality risk factors. Results: Two hundred and fifty-two adult patients with severe COVID-19 pneumonia enrolled in our study – 15.8% of patients died during hospitalization. The mortality rate was 57.5% for those over 65 years of age. 61.9% of patients had at least one coexisting disease. We revealed hemoglobin, leukocyte, lymphocyte, platelet, C-reactive protein, procalcitonin, d-dimer, aspartate aminotransferase, and alanine aminotransferase, lactate dehydrogenase, creatinine, and ferritin were significantly changing within the time and also between survivors and non-survivors. Conclusion: The study showed that blood cell counts, coagulation profiles, liver and kidney function tests, and inflammatory markers deteriorated in non-survivor COVID-19 patients. Patients with shortness of breath, history of congestive heart failure, coronary artery disease, dementia, chronic renal disease, higher Charlson comorbidity index score, the need for invasive mechanic ventilation, presence of acute respiratory distress syndrome, and intensive care unit admission are more vulnerable to death

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria

Rainfall induced landslides are a common threat to the communities living on dangerous hill-slopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence (WoE) method was applied to calculate the positive (presence of landslides) and negative (absence of landslides) factor weights. A combination of analytical hierarchical process (AHP) and fuzzy membership standardization (weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren’s algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of WoE, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively

GIS-based Landslide Susceptibility Modeling in the Blue Nile Gorge, Jema River Gorge and Debre Sina areas of the Central Ethiopian Highlands

Ehime University (愛媛大学)博士(工学)thesi

Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia

Abstract The study area is found in Gindeberet district of West Shewa zone in Oromia Regional State of Ethiopia.This area is highly susceptible to active surface processes due to the presence of rugged morphology with steep scarps, sharp ridges, cliffs, deep gorges and valleys. This study aimed to identify and evaluate the causative factors and to prepare the landslide susceptibility maps (LSMs) of the study area. Two bivariate statistical models i.e. Information value(IV) and the Frequency ratio(FR), were used. First, active, reactivated and passive landslides and scarps were identified using Google Earth image interpretation and extensive field survey for landslide inventory. A total of 580 landslide were randomly selected into two datasets in which (80%)460 landslides were used for modeling and (20%)116 landslidesfor validation. conditioning factors (slope, aspect, curvature, distance from stream, distance from lineaments, lithology, rainfall and land use) were combined with a training landslide dataset in a ArcGIS to generate LSMs which weredivided into verylow, low, moderate, high and veryhigh susceptibility zones. LSMs for IV and FR models were validated using the Area under(ROC) curve showing a success rate of 0.836 and 0.835 respectively and a predictive rate of 0.817 and 0.818 respectively wich showed a good performance of both models. The resulting LSMs can be used for land use planning and management.</jats:p

Weights of Evidence Modeling for Landslide Susceptibility Mapping of Kabi-Gebro Locality, Gundomeskel area, Central Ethiopia

Abstract Kabi-Gebro area is located within the Abay Basin at Dera District of North Shewa Zone near Gundomeskel town in the Central highland of Ethiopia and it is about 320 Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically characterized by weathered sedimentary and volcanic rocks. Currently, landslides are creating serious challenges in road construction, farming practices and affecting people in this area. Active landslides in this area damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map. To overcome the landslide problem in this area, landslide susceptibility map was prepared using GIS- based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslide locations were identified and classified randomly as training landslide (80%) and validation landslide (20%). The training landslide data set include nine landslide causative factors such as lithology, slope angle, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall inorder to prepare landslide susceptibility map in this study. The landslide susceptibility maps were prepared by adding the weights of contrast values of the nine causative factors using rater calculator in the spatial analyst tool of ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high landslide susceptiblity classes. This susceptibility map was validated using landslide density index and Area Under the Curve (AUC). The result from this validation showed a success rate and avalidaton rate accuracies of 82.4% and 83.4% respectively for this model. Finally, this study recommends application of appropriate mitigation or corrective measures in order to lessen the impact of landslide in the area.</jats:p

Stability analysis of rock slope along selected road sections from Gutane Migiru town to Fincha sugar factory, Oromiya, Ethiopia

AbstractThe slope instability was one of the common problems along the road that connects Gutane Migiru town to Fincha sugar factory, Western Ethiopia. The effect of the problem was intense mostly; during the rainy season, that triggers different modes of rock slope failure. As a result, the road was frequently damaged and blocked by the failed rock that in turn hinders the traffic activities. Thus, this study aimed at stability analyses of the critical slope sections using kinematic and limit equilibrium methods (LEM). The estimation of the most important input parameter in LEM analyses like cohesion and friction angle along the failure plane is often intricate and cumbersome. Hence, this paper used Rocscience software to effortlessly and instantly compute cohesion and friction angle along specific failure planes and then to carry out kinematic and LEM analyses. Besides, the strength of the intact rock was determined by the Schmidt hammer in the field and point load laboratory test. According to the kinematic analysis result, the wedge mode of rock slope failure occurred at slope sections D1S2 and D1S3 though the planar mode of failure occurred at slope sections D1S4 and D4S1. The factor of safety determined under all anticipated conditions became less than and greater than one at slope sections D1S2, D1S3, D1S4, and D4S1, and this depicts an unstable and stable slope, respectively. From the analysis result, the combined effect of rainfall, steepness of the slope dip, and joint set was the main factors that caused the slope insatiability.</jats:p

Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

Abstract Landslide susceptibility mapping is an important tool for disaster management and development activities such as planning of transportation infrastructure, settlement and agriculture. Shikoku Island, which is found in the southwest of Japan, is one of the most landslide prone areas because of heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models were applied in a GIS environment to prepare the landslide susceptibility maps of this area. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were carried out and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models with two, two and one were attempted respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points is slightly better than FRD and WoE models in predicting the future probability of landslide occurrence.</jats:p