Using OpenStreetMap point-of-interest data to model urban change-A feasibility study.
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ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC6388917 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundTo detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.ResultsIn this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study.ConclusionSimultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.
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Project description:Social media data has frequently sourced research on topics such as traveller planning or the factors that influence travel decisions. The literature on the location of tourist activities, however, is scarce. The studies in this line that do exist focus mainly on identifying points of interest and rarely on the urban areas that attract tourists. Specifically, as acknowledged in the literature, tourist attractions produce major imbalances with respect to adjacent urban areas. The present study aims to fill this research gap by addressing a twofold objective. The first was to design a methodology allowing to identify the preferred tourist areas based on concentrations of places and activities. The tourist area was delimited using Instasights heatmaps information and the areas of interest were identified by linking data from the location-based social network Foursquare to TripAdvisor’s database. The second objective was to delimit areas of interest based on users’ existing urban dynamics. The method provides a thorough understanding of functional diversity and the location of a city’s different functions. In this way, it contributes to a better understanding of the spatial distribution imbalances of tourist activities. Tourist areas of interest were revealed via the identification of users’ preferences and experiences. A novel methodology was thus created that can be used in the design of future tourism strategies or, indeed, in urban planning. The city of Bucharest, Romania, was taken as a case study to develop this exploratory research. Supplementary Information The online version contains supplementary material available at 10.1007/s40558-022-00235-8.
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Project description:Progressive and insidious cognitive decline that interferes with daily life is the defining characteristic of Alzheimer's disease (AD). Epidemiological studies have found that the pathological process of AD begins years before a clinical diagnosis is made and can be highly variable within a given population. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease-modifying therapies may be most effective. In the last decade, there has been an increased interest in the application of change-point models to longitudinal cognitive outcomes prior to and after diagnosis. Most of the proposed statistical methodology for describing decline relies upon distributional assumptions that may not hold. In this article, we introduce a quantile regression with a change-point model for longitudinal data of cognitive function in persons bound to develop AD. A change-point in our model reflects the transition from the cognitive decline due to normal aging to the accelerated decline due to disease progression. Quantile regression avoids common distributional assumptions on cognitive outcomes and allows the covariate effects and the change-point to vary for different quantiles of the response. We provided an approach for estimating the model parameters, including the change-point, and presented inferential procedures based on the asymptotic properties of the estimators. A simulation study showed that the estimation and inferential procedures perform reasonably well in finite samples. The practical use of our model was illustrated by an application to longitudinal episodic memory outcomes from two cohort studies of aging and AD.
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Project description:BackgroundFalls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication.ObjectiveThe objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods.MethodsAs a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest).ResultsIn total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models.ConclusionsTo enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.
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Project description:BackgroundInterventions having a strong theoretical basis are more efficacious, providing a strong argument for incorporating theory into intervention planning. The objective of this study was to develop a conceptual model to facilitate the planning of dietary intervention strategies at the household level in rural Kerala.MethodsThree focus group discussions and 17 individual interviews were conducted among men and women, aged between 23 and 75 years. An interview guide facilitated the process to understand: 1) feasibility and acceptability of a proposed dietary behaviour change intervention; 2) beliefs about foods, particularly fruits and vegetables; 3) decision-making in households with reference to food choices and access; and 4) to gain insights into the kind of intervention strategies that may be practical at community and household level. The data were analysed using a modified form of qualitative framework analysis, which combined both deductive and inductive reasoning. A priori themes were identified from relevant behaviour change theories using construct definitions, and used to index the meaning units identified from the primary qualitative data. In addition, new themes emerging from the data were included. The associations between the themes were mapped into four main factors and its components, which contributed to construction of the conceptual model.ResultsThirteen of the a priori themes from three behaviour change theories (Trans-theoretical model, Health Belief model and Theory of Planned Behaviour) were confirmed or slightly modified, while four new themes emerged from the data. The conceptual model had four main factors and its components: impact factors (decisional balance, risk perception, attitude); change processes (action-oriented, cognitive); background factors (personal modifiers, societal norms); and overarching factors (accessibility, perceived needs and preferences), built around a three-stage change spiral (pre-contemplation, intention, action). Decisional balance was the strongest in terms of impacting the process of behaviour change, while household efficacy and perceived household cooperation were identified as 'markers' for stages-of-change at the household level.ConclusionsThis type of framework analysis made it possible to develop a conceptual model that could facilitate the design of intervention strategies to aid a household-level dietary behaviour change process.
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Project description:The recent genome-wide allele-specific copy number variation data enable us to explore two types of genomic information including chromosomal genotype variations as well as DNA copy number variations. For a cancer study, it is common to collect data for paired normal and tumor samples. Then, two types of paired data can be obtained to study a disease subject. However, there is a lack of methods for a simultaneous analysis of these four sequences of data. In this study, we propose a statistical framework based on the change-point analysis approach. The validity and usefulness of our proposed statistical framework are demonstrated through the simulation studies and applications based on an experimental data set.
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