Modeling user mobility via user psychological and geographical behaviors towards point of-interest recommendation

© Springer International Publishing Switzerland 2016. The pervasive employments of Location-based Social Network call for precise and personalized Point-of-Interest (POI) recommendation to predict which places the users prefer. Modeling user mobility, as an important component of understanding user preference, plays an essential role in POI recommendation. However, existing methods mainly model user mobility through analyzing the check-in data and formulating a distribution without considering why a user checks in at a specific place from psychological perspective. In this paper, we propose a POI recommendation algorithm modeling user mobility by considering check-in data and geographical information. Specifically, with check-in data, we propose a novel probabilistic latent factor model to formulate user psychological behavior from the perspective of utility theory, which could help reveal the inner information underlying the comparative choice behaviors of users. Geographical behavior of all the historical check-ins captured by a power law distribution is then combined with probabilistic latent factor model to form the POI recommendation algorithm. Extensive evaluation experiments conducted on two real-world datasets confirm the superiority of our approach over state-of-the-art methods

Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia

Analysis of patient flows for orthopedic procedures using small area analysis in Switzerland

BACKGROUND: In general cantons regulate and control the Swiss health service system; patient flows within and between cantons are thereby partially disregarded. This paper develops an alternative spatial model, based upon the construction of orthopedic hospital service areas (HSA(O)s), and introduces indices for the analysis of patient streams in order to identify areas, irrespective of canton, with diverse characteristics, importance, needs, or demands. METHODS: HSA(O)s were constructed using orthopedic discharge data. Patient streams between the HSA(O)s were analysed by calculating three indices: the localization index (% local residents discharged locally), the netindex (the ratio of discharges of nonlocal incoming residents to outgoing local residents), and the market share index (% of local resident discharges of all discharges in local hospitals). RESULTS: The 85 orthopedic HSA(O)s show a median localization index of 60.8%, a market share index of 75.1%, and 30% of HSA(O)s have a positive netindex. Insurance class of bed, admission type, and patient age are partially but significantly associated with those indicators. A trend to more centrally provided health services can be observed not only in large urban HSA(O)s such as Geneva, Bern, Basel, and Zurich, but also in HSA(O)s in mountain sport areas such as Sion, Davos, or St.Moritz. Furthermore, elderly and emergency patients are more frequently treated locally than younger people or those having elective procedures. CONCLUSION: The division of Switzerland into HSA(O)s provides an alternative spatial model for analysing and describing patient streams for health service utilization. Because this small area model allows more in-depth analysis of patient streams both within and between cantons, it may improve support and planning of resource allocation of in-patient care in the Swiss healthcare system

Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR)

<p>Abstract</p> <p>Background</p> <p>Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.</p> <p>Methods</p> <p>We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected <it>a priori </it>determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.</p> <p>Application</p> <p>We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.</p> <p>Conclusion</p> <p>This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.</p

Prion Protein Is a Key Determinant of Alcohol Sensitivity through the Modulation of N-Methyl-D-Aspartate Receptor (NMDAR) Activity

The prion protein (PrP) is absolutely required for the development of prion diseases; nevertheless, its physiological functions in the central nervous system remain elusive. Using a combination of behavioral, electrophysiological and biochemical approaches in transgenic mouse models, we provide strong evidence for a crucial role of PrP in alcohol sensitivity. Indeed, PrP knock out (PrP−/−) mice presented a greater sensitivity to the sedative effects of EtOH compared to wild-type (wt) control mice. Conversely, compared to wt mice, those over-expressing mouse, human or hamster PrP genes presented a relative insensitivity to ethanol-induced sedation. An acute tolerance (i.e. reversion) to ethanol inhibition of N-methyl-D-aspartate (NMDA) receptor-mediated excitatory post-synaptic potentials in hippocampal slices developed slower in PrP−/− mice than in wt mice. We show that PrP is required to induce acute tolerance to ethanol by activating a Src-protein tyrosine kinase-dependent intracellular signaling pathway. In an attempt to decipher the molecular mechanisms underlying PrP-dependent ethanol effect, we looked for changes in lipid raft features in hippocampus of ethanol-treated wt mice compared to PrP−/− mice. Ethanol induced rapid and transient changes of buoyancy of lipid raft-associated proteins in hippocampus of wt but not PrP−/− mice suggesting a possible mechanistic link for PrP-dependent signal transduction. Together, our results reveal a hitherto unknown physiological role of PrP on the regulation of NMDAR activity and highlight its crucial role in synaptic functions

Detour and break optimising distance, a new perspective on transport and urbanism

International audienceFrom a discussion about the mathematical properties of metrics, we identify three fundamental characteristics of distance, which are optimality, detour and break. We then explore the implications of these properties for transport planning, urbanism and spatial planning. We state that distances contain the idea of optimum and that any distance is associated to a search for optimisation. Pedestrian movements obey this principle and sometimes depart from designed routes. Local sub-optimality conveyed by public transport maps has to be corrected by interventions on public space to relieve the load on central parts of networks. The second principle we state is that detour in distances is most often a means to optimise movement. Fast transport systems generates most of the detour observed in geographical spaces at regional scale. This is why detour has to be taken into account in regional transport policies. The third statement is that breaks in movement contribute to optimising distances. Benches, cafés, pieces of art, railway stations are examples of the urban break. These facilities of break represent an urban paradox: they organise the possibility of a break, of a waste of time in a trip, and they also contribute to optimising distances in a wider network. In that sense break should be considered as a relevant principle for the design of urban space in order to support a pedestrian oriented urban form

Determinants of the Incidence of Hand, Foot and Mouth Disease in China Using Geographically Weighted Regression Models

Child population density and climate factors are potential determinants of the HFMD incidence in most areas in China. The strength and direction of association between these factors and the incidence of HFDM is spatially heterogeneous at the local geographic level, and child population density has a greater influence on the incidence of HFMD than the climate factors

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725

Ecological Determinants of Highly Pathogenic Avian Influenza (H5N1) Outbreaks in Bangladesh

BACKGROUND: The agro-ecology and poultry husbandry of the south Asian and south-east Asian countries share common features, however, with noticeable differences. Hence, the ecological determinants associated with risk of highly pathogenic avian influenza (HPAI-H5N1) outbreaks are expected to differ between Bangladesh and e.g., Thailand and Vietnam. The primary aim of the current study was to establish ecological determinants associated with the risk of HPAI-H5N1 outbreaks at subdistrict level in Bangladesh. The secondary aim was to explore the performance of two different statistical modeling approaches for unmeasured spatially correlated variation. METHODOLOGY/PRINCIPAL FINDINGS: An ecological study at subdistrict level in Bangladesh was performed with 138 subdistricts with HPAI-H5N1 outbreaks during 2007-2008, and 326 subdistricts with no outbreaks. The association between ecological determinants and HPAI-H5N1 outbreaks was examined using a generalized linear mixed model. Spatial clustering of the ecological data was modeled using 1) an intrinsic conditional autoregressive (ICAR) model at subdistrict level considering their first order neighbors, and 2) a multilevel (ML) model with subdistricts nested within districts. Ecological determinants significantly associated with risk of HPAI-H5N1 outbreaks at subdistrict level were migratory birds' staging areas, river network, household density, literacy rate, poultry density, live bird markets, and highway network. Predictive risk maps were derived based on the resulting models. The resulting models indicate that the ML model absorbed some of the covariate effect of the ICAR model because of the neighbor structure implied in the two different models. CONCLUSIONS/SIGNIFICANCE: The study identified a new set of ecological determinants related to river networks, migratory birds' staging areas and literacy rate in addition to already known risk factors, and clarified that the generalized concept of free grazing duck and duck-rice cultivation interacted ecology are not significant determinants for Bangladesh. These findings will refine current understanding of the HPAI-H5N1 epidemiology in Bangladesh