Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?

Human activity type inference has long been the focus for applications ranging from managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing volume of mobility data allow researchers to explore a wide range of methodological approaches for this task. Such data, however, lack reference observations that would allow the validation of methodological approaches. This research proposes a methodological framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that can be applied to mobility data of low spatiotemporal resolution. The method was validated using open source Foursquare data under different isochrone configurations. The results provide evidence of the limits of activity detection accuracy using such data as determined by the Area Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time, results demonstrate that a hierarchical modeling framework can provide some flexibility against the challenges related to the nature of unsupervised activity classification using trajectory variables and POIs as input

Essays on economic forecasting with alternative datasets

This thesis contributes to the field of economic forecasting using alternative datasets. The first study explores the benefits of search data for nowcasting GDP growth in the U.S. and Brazil, focusing on the marginal contribution of Google Trends compared to macroeconomic predictors. We use a dynamic factor model to address the large number of predictors and the “ragged-edge” problem. Our findings reveal that factor models incorporating Google “categories” data provide advantages over traditional models, with similar benefits observed in both economies regardless of the variable-selection strategy in the factor model. Using more detailed Google Trends data beyond its predefined “categories” does not yield additional benefits. In the second study, we assess the potential of internet search data to enhance forecasts of private consumption and its components. Commencing with an initial set of consumption-related keywords, we construct three Google Trends datasets, encompassing search queries semantically related to the original terms. Employing various models suitable for high-dimensional structures, we show that Google Trends effectively forecasts aggregate private consumption, especially over long-term horizons and for durable goods post-pandemic, with random forests proving the most effective. The final chapter examines if alternative predictors from internet searches and news articles can improve forecasts of inflation uncertainty in the United States. We create a novel set of predictors using Google Trends and Bloomberg’s News Trends with inflation-related keywords. Three measures of inflation uncertainty are derived, reflecting disagreements in price expectations among households, investors, and professional forecasters. Results indicate significant forecast improvements for households’ uncertainty over short horizons from Google and News Trends. However, macroeconomic predictors remain more valuable for investors’ uncertainty, and neither data source effectively predicts professional forecasters’ uncertainty. Most benefits of using Google and News Trends to forecast households’ uncertainty have emerged recently, highlighting their importance during times of uncertainty

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data

Osteolytic bone destruction resulting from relapse of a testicular tumour 23 years after inguinal orchiectomy and adjuvant chemotherapy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Late relapse of a testicular germ cell tumour is an uncommon occurrence. We report a case of osteolytic bone metastasis appearing 23 years after the initial treatment of a metastatic testicular mixed tumour (choriocarcinoma and embryonal carcinoma). This is one of the longest periods of recurrence reported for testicular germ cell tumours.</p> <p>Case presentation</p> <p>A 52-year-old Caucasian man who underwent a right inguinal orchiectomy due to testicular tumour in 1984 presented to our outpatient clinic in a generally bad condition of health and with severe pain of his right hip joint and os ischii caused by osteolytic metastasis.</p> <p>Conclusions</p> <p>This case emphasizes the need for a life-long follow-up of patients with primary metastatic testicular cancer.</p

Perforation of the Urinary Bladder Caused by Transurethral Insertion of a Pencil for the Purpose of Masturbation in a 29-Year-Old Female

The urethra is a usual site of introduction of foreign bodies for autoerotic stimulation. We present an unusual case of bladder perforation caused by foreign body that was self-inserted in the urethra and consequently slipped inside the bladder in a 29-year-old female patient with psychiatric disease. The patient was referred to our department for macroscopic hematuria and abdominal pain. Imaging studies revealed the presence of a foreign body in the pelvic area which had perforated the left lateral wall of the bladder. The foreign body was removed via open cystotomy. In psychiatric patients hematuria and pelvic pain may result from insertion of a foreign body in the bladder usually during masturbation

A Case Report of Urinary Bladder Carcinosarcoma and Review of the Literature

Carcinosarcoma of the bladder is an unusual tumour characterized by a combination of malignant epithelial and soft tissue elements. Most of the reported cases have been case reports or small series. Optimal treatment is uncertain. We herein report our experience in such a case treated with transurethral resection followed by radiotherapy with adverse final outcome. Treatment of bladder carcinosarcomas should be aggressive and multimodal but optional treatment is still unknown. Radiotherapy alone is insufficient as a treatment option of these aggressive tumors

Massive hematuria due to a congenital renal arteriovenous malformation mimicking a renal pelvis tumor: a case report

<p>Abstract</p> <p>Introduction</p> <p>Congenital renal arteriovenous malformations (AVMs) are very rare benign lesions. They are more common in women and rarely manifest in elderly people. In some cases they present with massive hematuria. Contemporary treatment consists of transcatheter selective arterial embolization which leads to resolution of the hematuria whilst preserving renal parenchyma.</p> <p>Case presentation</p> <p>A 72-year-old man, who was heavy smoker, presented with massive hematuria and flank pain. CT scan revealed a filling defect caused by a soft tissue mass in the renal pelvis, which initially led to the suspicion of a transitional cell carcinoma (TCC) of the upper tract, in view of the patient's age and smoking habits. However a subsequent retrograde study could not depict any filling defect in the renal pelvis. Selective right renal arteriography confirmed the presence of a renal AVM by demonstrating abnormal arterial communication with a vein with early visualization of the venous system. At the same time successful selective transcatheter embolization of the lesion was performed.</p> <p>Conclusion</p> <p>This case highlights the importance of careful diagnostic work-up in the evaluation of upper tract hematuria. In the case presented, a congenital renal AVM proved to be the cause of massive upper tract hematuria and flank pain in spite of the initial evidence indicating the likely diagnosis of a renal pelvis tumor.</p

Survival Outcomes of Pancreatic Intraepithelial Neoplasm (PanIN) versus Intraductal Papillary Mucinous Neoplasm (IPMN) Associated Pancreatic Adenocarcinoma

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Pancreatic intraepithelial neoplasms (PanINs) and intraductal papillary mucinous neoplasms (IPMNs) are common pancreatic adenocarcinoma precursor lesions. However, data regarding their respective associations with survival rate and prognosis are lacking. We retrospectively evaluated 72 pancreatic adenocarcinoma tumor resection patients at the University of Kansas Hospital between August 2009 and March 2019. Patients were divided into one of two groups, PanIN or IPMN, based on the results of the surgical pathology report. We compared baseline characteristics, overall survival (OS), and progression free survival (PFS) between the two groups, as well as OS and PFS based on local or distant tumor recurrence for both groups combined. 52 patients had PanINs and 20 patients had IPMNs. Patients who had an IPMN precursor lesion had better median PFS and OS when compared to patients with PanIN precursor lesions. However, the location of tumor recurrence (local or distant) did not show a statistically significant difference in OS

The Use Of "Big Data" To Evaluate Accessibility Measures For Wheelchair And Mobility Scooter Users: The Case Of London Bus Network

As recognized by the Social Exclusion Unit poor transport access can contribute to the causes of social exclusion. People who require the use of a powered wheelchair or a mobility scooter in order to be able to carry out their daily activities are most likely to experience transport disadvantage, primarily due to architectural barriers present within the network that limit accessibility. Formulation of new policies is of primary importance to increase the transport network accessibility. Policy-making and evaluation are often based mainly on qualitative approaches. Although this can give a good appreciation of the users prospective, it fails to consider the more global impact that new and existing policies can have. The use of big datasets from automated fare collection systems could improve this aspect, allowing for a more quantitative approach to measuring accessibility measures. Furthermore these datasets could benefit from a more disaggregated classification to help categorise travel patterns and behaviour specific to wheelchairs and mobility scooter users