A brief visual primer for the mapping of mortality trend data

Maps are increasingly used to visualize and analyze data, yet the spatial ramifications of data structure are rarely considered. Data are subject to transformations made throughout the research process and then used to map, visualize and conduct spatial analysis. We used mortality data to answer three research questions: Are there spatial patterns to mortality, are these patterns statistically significant, and are they persistent across time? This paper provides differential spatial patterns by implementing six data transformations: standardization, cut-points, class size, color scheme, spatial significance and temporal mapping. We use numerous maps and graphics to illustrate the iterative nature of mortality mapping, and exploit the visual nature of the International Journal of Health Geographics journal on the World Wide Web to present researchers with a series of maps

User profile modelling based on mobile phone sensing and call logs

There are remaining questions concerning user profile modelling in the mobile advertising domain. The research question addressed in this paper is how to design a specific user profile model, that is a simplified model in terms of the amount of user data to be collected, that considers relevant aspects of mobile advertising such as social and personal context, and user privacy preservation. To address this question, a new user profile model consisting of three phases was proposed: (1) data collection, (2) integration and normalization of collected data, and (3) inference of knowledge about the mobile user’s profile. The most significant contributions of the proposed model are a simplified user profile model approach which tackles the dependency on other data sources like OSN platforms and local data gathering and storage that contributes to the user privacy-preserving since the user can exert more control over his/her personal data

From Traditional to Electrified Urban Road Networks: The Integration of Fuzzy Analytic Hierarchy Process and GIS as a Tool to Define a Feasibility Index—An Italian Case Study

To achieve sustainable development in the road sector, the use of Electric Vehicles (EVs) appears as a positive response to transport emissions. Among the available technologies, dynamic charging seems to overcome the main weakness points of EVs, even if it requires that traditional roads (t-roads) be equipped with a system providing electricity for EVs. Thus, so-called electrified roads (e-roads) must be implemented into the urban road networks. Since it is not possible to electrify all roads simultaneously, and also to consider the demand needs of citizens, a selection criterion is essential. This research describes and develops a simple, self-explanatory, repeatable, and adaptable selection criterion aimed at helping city managers in prioritizing the roads of an urban network to be upgraded from t-road to e-road status. This method belongs to the so-called Multicriteria Spatial Decision Support Systems (MC SDSS)—processes useful for solving spatial problems through the integration of multicriteria analysis (Fuzzy Analytic Hierarchy Process, F-AHP) with a geo referenced data management and analysis tool (GIS). The developed algorithm is based on several criteria related to the infrastructure/transport, social and environmental areas. The result of the implemented method is a Feasibility Index (FI), able to prioritize the roads most eligible to be upgraded as e-roads, as also verified by its application on the urban area of Milan (Italy)

Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC

Recently, the growing number of learners in Massive Open Online Course (MOOC) environments generate a vast amount of online comments via social interactions, general discussions, expressing feelings or asking for help. Concomitantly, learner dropout, at any time during MOOC courses, is very high, whilst the number of learners completing (completers) is low. Urgent intervention and attention may alleviate this problem. Analysing and mining learner comments is a fundamental step towards understanding their need for intervention from instructors. Here, we explore a dataset from a FutureLearn MOOC course. We find that (1) learners who write many comments that need urgent intervention tend to write many comments, in general. (2) The motivation to access more steps (i.e., learning resources) is higher in learners without many comments needing intervention, than that of learners needing intervention. (3) Learners who have many comments that need intervention are less likely to complete the course (13%). Therefore, we propose a new priority model for the urgency of intervention built on learner histories – past urgency, sentiment analysis and step access

Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesNowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hub

Expectation-Maximization Binary Clustering for Behavioural Annotation

We present a variant of the well sounded Expectation-Maximization Clustering algorithm that is constrained to generate partitions of the input space into high and low values. The motivation of splitting input variables into high and low values is to favour the semantic interpretation of the final clustering. The Expectation-Maximization binary Clustering is specially useful when a bimodal conditional distribution of the variables is expected or at least when a binary discretization of the input space is deemed meaningful. Furthermore, the algorithm deals with the reliability of the input data such that the larger their uncertainty the less their role in the final clustering. We show here its suitability for behavioural annotation of movement trajectories. However, it can be considered as a general purpose algorithm for the clustering or segmentation of multivariate data or temporal series.Comment: 34 pages main text including 11 (full page) figure

Responsive Management: Municipal Leadership for an Aging Population

This article focuses on the responsive management of municipal leadership by identifying organizational and community values that affect age friendly policy making. The data comes from a sample of 1050 cities extracted from a national list of cities identified as place geography on the U.S. census list of geographies. The web-based questionnaire explored policy choices of 331 respondents in the areas of mobility, housing, the built environment, and public service delivery administered between May and August 2016. The institutionalization of the needs of an aging population in city management principles results in high levels of age friendly policy action by cities. Public advocacy on aging issues enhances the impact on local policy-making. Safeguarding of public interest through city management suggests municipalities may adjust procedurally to respond to the needs of an aging population. Public managers may find opportunities to facilitate increased policy action and services to support a growing older adult population. There are implications for older people to age in place when their community lacks municipal leadership on aging in place policies and older people have limited voice on aging issues

Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá

[EN] Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people's health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogota is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study's approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.Many thanks to the members of the Intelligence and Territorial Analysis Group of the Universidad Santo Tomás for their collaboration in conducting the fieldwork.Molina-Gomez, NI.; Calderón-Rivera, DS.; Sierra-Parada, R.; Díaz Arévalo, JL.; López Jiménez, PA. (2021). 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