Effect of Physical Constraints on Spatial Connectivity in Urban Areas

Obstacle effect on proximity, connectivity, and organization of spatial data calls for derivation of measures that enable quantifying their influence. Provision of such measures is valuable for ensuring an aware planning, analysis of obstacle impact on spatial data, and the consequent placement of crossings. This paper proposes quantifying obstacle influence via their impact on connectivity and aggregation of data. As the paper shows, the derived indices enable capturing the actual obstacle effect on spatial data while accommodating datasets with different level of complexity. The information and contribution of these indices are demonstrated and analyzed, and results show how the derived measures reflect changes in spatial data arrangement

An Approach to Nearest Neighboring Search for Multi-dimensional Data

Finding nearest neighbors in large multi-dimensional data has always been one of the research interests in data mining field. In this paper, we present our continuous research on similarity search problems. Previously we have worked on exploring the meaning of K nearest neighbors from a new perspective in PanKNN [20]. It redefines the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. A large amount of real data sets have irrelevant or obstacle information which greatly affects the effectiveness and efficiency of finding nearest neighbors for a given query data point. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches

An Attempt to Find Neighbors

In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18]which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches

Place, time and experience: barriers to universalization of institutional child delivery in rural Mozambique

CONTEXT: Although institutional coverage of childbirth is increasing in the developing world, a substantial minority of births in rural Mozambique still occur outside of health facilities. Identifying the remaining barriers to safe professional delivery services can aid in achieving universal coverage. METHODS: Survey data collected in 2009 from 1,373 women in Gaza, Mozambique, were used in combination with spatial, meteorological and health facility data to examine patterns in place of delivery. Geographic information system–based visualization and mapping and exploratory spatial data analysis were used to outline the spatial distribution of home deliveries. Multilevel logistic regression models were constructed to identify associations between individual, spatial and other characteristics and whether women's most recent delivery took place at home. RESULTS: Spatial analysis revealed high- and low-prevalence clusters of home births. In multivariate analyses, women with a higher number of clinics within 10 kilometers of their home had a reduced likelihood of home delivery, but those living closer to urban centers had an increased likelihood. Giving birth during the rainy, high agricultural season was positively associated with home delivery, while household wealth was negatively associated with home birth. No associations were evident for measures of exposure to and experience with health institutions. CONCLUSIONS: The results suggest the need for a comprehensive approach to expansion of professional delivery services. Such an approach should complement measures facilitating physical access to health institutions for residents of harder-to-reach areas with community-based interventions aimed at improving rural women's living conditions and opportunities, while also taking into account seasonal and other variables

The influence of the built environment on walking among urban, community-dwelling older adults in the United States: A systematic review and thematic synthesis

Background and Purpose: Driven by changing demographics, cultural shifts, and improvements in healthcare, the older adult population (≥65 years) in the United States (U.S.) is growing at a rapid and unprecedented rate. Simultaneously, the U.S, is becoming increasingly urbanized, with projections indicating that 87% of the U.S. population will live in urban centers by 2050. The convergence of an urbanizing and aging population necessitates a focus on aligning the urban environment with the needs of older adults. Among these needs is continued physical activity (PA) throughout older adulthood, yet despite the numerous physical, mental, and social benefits, the majority of older adults fail to meet recommended PA guidelines. Objectives: To investigate the influence of the outdoor built environment on walking behaviors among urban, community-dwelling older adults (≥65 years) in the U.S. and to analyze results with reference to implications for designing and improving communities to support active living for this population. Methods: A systematic review was conducted following PRISMA guidelines. Eight databases were searched for articles published 2011-2016 using search terms related to features of built environment, older adults, and walking. A total of 3254 abstracts were reviewed for eligibility, of which 17 studies fit all inclusion criteria. The final study set underwent quality appraisal, followed by data extraction, analysis, and thematic synthesis. Results: Findings suggest that older adults engage in PA for two primary purposes, leisure or transport, and that unique aspects of the built environment support or act as a barrier to each type of walking. Macroscale components of walkability (e.g., land use mix, street connectivity, and population density) were consistently and positively associated with transport walking, whereas microscale elements, such as aesthetics, pedestrian infrastructure, and crossing characteristics, were more commonly associated with leisure walking. Conclusions: Results support multiple associations between the built environment and walking behavior among older adults. This review furthers the current evidence regarding how macro and microscale features of the built environment can act upon older adults to support or discourage walking. While this area of research is growing, numerous gaps in the literature were identified. Future research is needed to improve the generalizability of findings to better inform future interventions and policies that support this population

Agent-based model of broadband adoption in unserved and underserved areas

In the last two decades, demand for broadband internet has far outpaced its availability. The Federal Communications Commission’s (FCC) 2020 Broadband Deployment report suggests that at least 22 million Americans living in rural areas lack access to broadband internet. With the COVID-19 pandemic affecting normal life, there is an overwhelming need to enable unserved and underserved communities to adapt to the “new normal”. To address this challenge, federal and state agencies are funding internet service providers (ISPs) to deploy infrastructure in rural communities. However, policymakers and ISPs need open-source tools to predict take-rates of broadband service and formulate effective strategies to increase the adoption of high-speed internet. We propose using an agent-based model grounded in “The Theory of Planned Behavior” -- a long-established behavioral theory that explains the consumer’s decision-making process. The model simulates residential broadband adoption by capturing the interaction of a broadband service’s attributes with consumer preferences. We demonstrate the model’s performance, present a case study of an unserved area, and perform a sensitivity analysis. The major findings support the appropriateness of using theoretically based agent-based models to predict take-rates of broadband service. We also find that the take-rates are highly influenced by presence of existing internet users in the area as well as affordable or subsidized prices. In the future, this model can be extended to study the impact of online education, telecommuting, telemedicine, and precision agriculture on a rural economy. This type of simulation can guide evidence-based decision-making for infrastructure investment based on demand as well as influence the design of market subsidies that aim to reduce the digital divide --Abstract, page iii

The neighbourhood physical environment and active travel in older adults : a systematic review and meta-analysis

BACKGROUND: Perceived and objectively-assessed aspects of the neighbourhood physical environment have been postulated to be key contributors to regular engagement in active travel (AT) in older adults. We systematically reviewed the literature on neighbourhood physical environmental correlates of AT in older adults and applied a novel meta-analytic approach to statistically quantify the strength of evidence for environment-AT associations. METHODS: Forty two quantitative studies that estimated associations of aspects of the neighbourhood built environment with AT in older adults (aged ≥ 65 years) and met selection criteria were reviewed and meta-analysed. Findings were analysed according to five AT outcomes (total walking for transport, within-neighbourhood walking for transport, combined walking and cycling for transport, cycling for transport, and all AT outcomes combined) and seven categories of the neighbourhood physical environment (residential density/urbanisation, walkability, street connectivity, access to/availability of services/destinations, pedestrian and cycling infrastructure, aesthetics and cleanliness/order, and safety and traffic). RESULTS: Most studies examined correlates of total walking for transport. A sufficient amount of evidence of positive associations with total walking for transport was found for residential density/urbanisation, walkability, street connectivity, overall access to destinations/services, land use mix, pedestrian-friendly features and access to several types of destinations. Littering/vandalism/decay was negatively related to total walking for transport. Limited evidence was available on correlates of cycling and combined walking and cycling for transport, while sufficient evidence emerged for a positive association of within-neighbourhood walking with pedestrian-friendly features and availability of benches/sitting facilities. Correlates of all AT combined mirrored those of walking for transport. Positive associations were also observed with food outlets, business/institutional/industrial destinations, availability of street lights, easy access to building entrance and human and motorised traffic volume. Several but inconsistent individual- and environmental-level moderators of associations were identified. CONCLUSIONS: Results support strong links between the neighbourhood physical environment and older adults’ AT. Future research should focus on the identification of types and mixes of destinations that support AT in older adults and how these interact with individual characteristics and other environmental factors. Future research should also aim to clarify dose-response relationships through multi-country investigations and data-pooling from diverse geographical regions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-017-0471-5) contains supplementary material, which is available to authorized users

Adaptive constrained clustering with application to dynamic image database categorization and visualization.

The advent of larger storage spaces, affordable digital capturing devices, and an ever growing online community dedicated to sharing images has created a great need for efficient analysis methods. In fact, analyzing images for the purpose of automatic categorization and retrieval is quickly becoming an overwhelming task even for the casual user. Initially, systems designed for these applications relied on contextual information associated with images. However, it was realized that this approach does not scale to very large data sets and can be subjective. Then researchers proposed methods relying on the content of the images. This approach has also proved to be limited due to the semantic gap between the low-level representation of the image and the high-level user perception. In this dissertation, we introduce a novel clustering technique that is designed to combine multiple forms of information in order to overcome the disadvantages observed while using a single information domain. Our proposed approach, called Adaptive Constrained Clustering (ACC), is a robust, dynamic, and semi-supervised algorithm. It is based on minimizing a single objective function incorporating the abilities to: (i) use multiple feature subsets while learning cluster independent feature relevance weights; (ii) search for the optimal number of clusters; and (iii) incorporate partial supervision in the form of pairwise constraints. The content of the images is used to extract the features used in the clustering process. The context information is used in constructing a set of appropriate constraints. These constraints are used as partial supervision information to guide the clustering process. The ACC algorithm is dynamic in the sense that the number of categories are allowed to expand and contract depending on the distribution of the data and the available set of constraints. We show that the proposed ACC algorithm is able to partition a given data set into meaningful clusters using an adaptive, soft constraint satisfaction methodology for the purpose of automatically categorizing and summarizing an image database. We show that the ACC algorithm has the ability to incorporate various types of contextual information. This contextual information includes: spatial information provided by geo-referenced images that include GPS coordinates pinpointing their location, temporal information provided by each image\u27s time stamp indicating the capture time, and textual information provided by a set of keywords describing the semantics of the associated images