Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Framework for developing and deploying location-based services in emerging economies

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.Includes bibliographical references.The general belief is that Location-Based Services (LBS) in emerging economies does not make much sense until there is widespread availability of geographic information system (GIS) data, broadband internet access, payment methods, infrastructure such as power, well developed advertising platform, etc. There is also the belief that these deficiencies make it next to impossible to realize revenues from the existing revenue models such as mobile adverts, online adverts, subscription, etc. This study shows how LBS services can be developed and deployed in emerging economies within these given set of constraints. It also adduces methods for overcoming some of the identified hindrances such as ways for creating effective and sufficient revenues from online and mobile adverts. The central hypothesis for this work is encapsulated in a "change of mindset" from developing products comparable to those in developed world (United States, Western Europe) to developing products which meet the immediate needs of the local environment in emerging economies/developing economies (however crude these solutions may appear initially from the POV of the developed world) and make use of not only locally available technologies but locally available phenomena. These solutions are then refined as they are consumed by the populace and the populace becomes more "sophisticated". This hypothesis is developed and fleshed out in a methodical manner using data and examples from developing countries - Nigeria (Africa), India (Asia), etc. This study finishes with the architecting of an LBS service (routing/navigation service) for an emerging economy using the framework developed in this study. Recommended future work includes developing more LBS services using this framework and deployment of the developed service, followed by a detailed analysis of the framework and possibly refinements to it.by Ifeanyi Katchy.S.M

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

The Effect of Implementing Behavioral Counseling for Elevated LDL Levels

Hyperlipidemia is a key risk factor in cardiovascular mortality, and is prevalent in approximately 38% of American adults (CDC, 2022b). Cholesterol levels are intensified by unhealthy lifestyle choices, which means a change in lifestyle behaviors could prevent cardiovascular related deaths (WHO, 2022). The PICOT question for this project was: In adults aged 20 years or older in the primary care setting who have elevated low-density lipoprotein (LDL) levels (P), does the implementation of behavioral counseling on lifestyle modifications (I) compared to current practice (C) decrease LDL levels (O) over a 10- to 12- week period (T)? Fourteen participants from a small direct primary care clinic in Indiana completed the entirety of the project. LDL levels were measured pre-intervention, along with a rapid assessment of physical activity (RAPA) and the rate your plate (RYP) tool, blood pressure (BP), body mass index (BMI), weight, and atherosclerotic cardiovascular disease (ASCVD) risk scores. The nurse practitioner initiated a behavioral counseling session on lifestyle modifications, assisted by educational handouts, and created three healthy goals with the participant. A follow-up telephone counseling session was scheduled at five weeks to review those goals, followed by an in-person counseling session at 10- to 12- weeks. LDL levels were redrawn and the RYP and RAPA tools, weight, BMI, BP and ASCVD scores were completed once more to show a within-group evaluation of pre- and post-intervention outcomes. A paired t-test was used for analysis, and statistically significant data was found by increased RYP scores (p = .001), increased RAPA scores (p = .004), weight reduction (p = .035), BMI reduction (p = .026), systolic BP reduction (p = .025), and ASCVD score reduction (p = .002). There was no statistical significance in LDL reduction (p = .051); however, there was still a decrease in mean scores pre- (137.36) and post- (114.43) intervention. These findings support the use of behavioral counseling for lifestyle modifications in patients with elevated LDL levels

Abstraction and cartographic generalization of geographic user-generated content: use-case motivated investigations for mobile users

On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the data

Using location-based services to improve mental health interventions

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe rapid developments in the functionalities of smartphones and technological innovations play a vital role in providing location-based services in healthcare. A mental health sensor-based software platform has been developed by the Geospatial Technologies research group (Geotec), consisting of an application generation framework that offers basic geospatial building blocks (location tracking, trajectory recording, geo-fencing), communication building blocks (notifications) and a basic visualization of collected data for therapists. The framework has been successfully tested for building an application to treat agoraphobia, addiction, and depression, using location-based notifications. However, defining the places of interest for a patient is addressed to a limited extent only. Thus, therapists have difficulties of identifying and defining multiple places of interest, and the generated apps were therefore mostly limited to single places of interest, which were manually defined. Hence, they are difficult to use in larger areas. This thesis aims to use a location-based service to support therapists in defining places of interest, based on location and place categories. The work is carried out as an extension of the SYMPTOMS platform, and it allows therapists to define multiple places of interest automatically and for larger areas. The added value of the approach (in terms of automation, ease of use, and universally usable of therapies) by the location-based services in improving mental health interventions is evaluated. As a result, the application was found to be usable with SUS score of 91.875 and useful for therapists to define multiple places of interest at the same time which simplifies the configuration process and makes therapies universally usable. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 1, 2, 2 (input data, pre-processing, methods, computational environment, results)

Protecting privacy of semantic trajectory

The growing ubiquity of GPS-enabled devices in everyday life has made large-scale collection of trajectories feasible, providing ever-growing opportunities for human movement analysis. However, publishing this vulnerable data is accompanied by increasing concerns about individuals’ geoprivacy. This thesis has two objectives: (1) propose a privacy protection framework for semantic trajectories and (2) develop a Python toolbox in ArcGIS Pro environment for non-expert users to enable them to anonymize trajectory data. The former aims to prevent users’ re-identification when knowing the important locations or any random spatiotemporal points of users by swapping their important locations to new locations with the same semantics and unlinking the users from their trajectories. This is accomplished by converting GPS points into sequences of visited meaningful locations and moves and integrating several anonymization techniques. The second component of this thesis implements privacy protection in a way that even users without deep knowledge of anonymization and coding skills can anonymize their data by offering an all-in-one toolbox. By proposing and implementing this framework and toolbox, we hope that trajectory privacy is better protected in research

Urban Air Mobility Market Study

The Booz Allen Team explored market size and potential barriers to Urban Air Mobility (UAM) by focusing on three potential markets – Airport Shuttle, Air Taxi, and Air Ambulance. We found that the Airport Shuttle and Air Taxi markets are viable, with a significant total available market value in the U.S. of $500 billion, for a fully unconstrained scenario. In this unconstrained best-case scenario, passengers would have the ability to access and fly a UAM at any time, from any location to any destination, without being hindered by constraints such as weather, infrastructure, or traffic volume. Significant legal and regulatory, weather, certification, public perception, and infrastructure constraints exist, which reduce the market potential for these applications to only about 0.5$2.5 billion, in the near term. However, we determined that these constraints can be addressed through ongoing intra-governmental partnerships, government and industry collaboration, strong industry commitment, and existing legal and regulatory enablers. We found that the Air Ambulance market is not a viable market if served by electric vertical takeoff and landing (eVTOL) vehicles due to technology constraints but may potentially be viable if a hybrid VTOL aircraft are utilized