ResearchPad - geographic-information-systems https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Mesh smoothing algorithm based on exterior angles split]]> https://www.researchpad.co/article/elastic_article_13823 Since meshes of poor quality give rise to low accuracy in finite element analysis and kinds of inconveniences in many other applications, mesh smoothing is widely used as an essential technique for the improvement of mesh quality. With respect to this issue, the main contribution of this paper is that a novel mesh smoothing method based on an exterior-angle-split process is proposed. The proposed method contains three main stages: the first stage is independent element geometric transformation performed by exterior-angle-split operations, treating elements unconnected; the second stage is to offset scaling and displacement induced by element transformation; the third stage is to determine the final positions of nodes with a weighted strategy. Theoretical proof describes the regularity of this method and many numerical experiments illustrate its convergence. Not only is this method applicable for triangular mesh, but also can be naturally extended to arbitrary polygonal surface mesh. Quality improvements of demonstrations on triangular and quadrilateral meshes show the effectiveness of this method.

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<![CDATA[Phylogeographic investigation of 2014 porcine epidemic diarrhea virus (PEDV) transmission in Taiwan]]> https://www.researchpad.co/article/5c89779dd5eed0c4847d319c

The porcine epidemic diarrhea virus (PEDV) that emerged and spread throughout Taiwan in 2014 triggered significant concern in the country’s swine industry. Acknowledging the absence of a thorough investigation at the geographic level, we used 2014 outbreak sequence information from the Taiwan government’s open access databases plus GenBank records to analyze PEDV dissemination among Taiwanese pig farms. Genetic sequences, locations, and dates of identified PEDV-positive cases were used to assess spatial, temporal, clustering, GIS, and phylogeographic factors affecting PEDV dissemination. Our conclusion is that S gene sequences from 2014 PEDV-positive clinical samples collected in Taiwan were part of the same Genogroup 2 identified in the US in 2013. According to phylogenetic and phylogeographic data, viral strains collected in different areas were generally independent of each other, with certain clusters identified across different communities. Data from GIS and multiple potential infection factors were used to pinpoint cluster dissemination in areas with large numbers of swine farms in southern Taiwan. The data indicate that the 2014 Taiwan PEDV epidemic resulted from the spread of multiple strains, with strong correlations identified with pig farm numbers and sizes (measured as animal concentrations), feed mill numbers, and the number of slaughterhouses in a specifically defined geographic area.

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<![CDATA[Optimizing community screening for tuberculosis: Spatial analysis of localized case finding from door-to-door screening for TB in an urban district of Ho Chi Minh City, Viet Nam]]> https://www.researchpad.co/article/5c22a0c3d5eed0c4849ec15b

Background

Tuberculosis (TB) is the deadliest infectious disease globally. Current case finding approaches may miss many people with TB or detect them too late.

Data and methods

This study was a retrospective, spatial analysis of routine TB surveillance and cadastral data in Go Vap district, Ho Chi Minh City. We geocoded TB notifications from 2011 to 2015 and calculated theoretical yields of simulated door-to-door screening in three concentric catchment areas (50m, 100m, 200m) and three notification window scenarios (one, two and four quarters) for each index case. We calculated average yields, compared them to published reference values and fit a GEE (Generalized Estimating Equation) linear regression model onto the data.

Results

The sample included 3,046 TB patients. Adjusted theoretical yields in 50m, 100m and 200m catchment areas were 0.32% (95%CI: 0.27,0.37), 0.21% (95%CI: 0.14,0.29) and 0.17% (95%CI: 0.09,0.25), respectively, in the baseline notification window scenario. Theoretical yields in the 50m-catchment area for all notification window scenarios were significantly higher than a reference yield from literature. Yield was positively associated with treatment failure index cases (beta = 0.12, p = 0.001) and short-term inter-province migrants (beta = 0.06, p = 0.022), while greater distance to the DTU (beta = -0.02, p<0.001) was associated with lower yield.

Conclusions

This study is an example of inter-departmental collaboration and application of repurposed cadastral data to progress towards the end TB objectives. The results from Go Vap showed that the use of spatial analysis may be able to identify areas where targeted active case finding in Vietnam can help improve TB case detection.

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<![CDATA[Spatio-Temporal Factors Associated with Meningococcal Meningitis Annual Incidence at the Health Centre Level in Niger, 2004&#8211;2010]]> https://www.researchpad.co/article/5989da34ab0ee8fa60b85c9b

Background

Epidemics of meningococcal meningitis (MM) recurrently strike the African Meningitis Belt. This study aimed at investigating factors, still poorly understood, that influence annual incidence of MM serogroup A, the main etiologic agent over 2004–2010, at a fine spatial scale in Niger.

Methodology/Principal Findings

To take into account data dependencies over space and time and control for unobserved confounding factors, we developed an explanatory Bayesian hierarchical model over 2004–2010 at the health centre catchment area (HCCA) level. The multivariate model revealed that both climatic and non-climatic factors were important for explaining spatio-temporal variations in incidence: mean relative humidity during November–June over the study region (posterior mean Incidence Rate Ratio (IRR) = 0.656, 95% Credible Interval (CI) 0.405–0.949) and occurrence of early rains in March in a HCCA (IRR = 0.353, 95% CI 0.239–0.502) were protective factors; a higher risk was associated with the percentage of neighbouring HCCAs having at least one MM A case during the same year (IRR = 2.365, 95% CI 2.078–2.695), the presence of a road crossing the HCCA (IRR = 1.743, 95% CI 1.173–2.474) and the occurrence of cases before 31 December in a HCCA (IRR = 6.801, 95% CI 4.004–10.910). At the study region level, higher annual incidence correlated with greater geographic spread and, to a lesser extent, with higher intensity of localized outbreaks.

Conclusions

Based on these findings, we hypothesize that spatio-temporal variability of MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk, and is further impacted by factors of spatial contacts, representing facilitated pathogen transmission. Additional unexplained factors may contribute to the observed incidence patterns and should be further investigated.

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<![CDATA[Socio-environmental exposures and health outcomes among persons with sickle cell disease]]> https://www.researchpad.co/article/5989db51ab0ee8fa60bdc2ea

There is much variability in the expression of sickle cell disease (SCD) and recent works suggest that environmental and social factors may also influence this variability. This paper aims to use geographic information systems technology to examine the association between socio-environmental exposures and health outcomes in all persons who have attended or currently attend the Sickle Cell Unit in Jamaica. Rural patients presented for clinical care at older ages and had less annual visits to clinic. Persons travelled relatively long distances to seek SCD care and those travelling longer had less health maintenance visits. Urban patients had a higher prevalence of significant pain crises (69.4% vs. 55.8%, p value<0.001) and respiratory events (21.2% vs. 14%, p value<0.001). Prevalence of leg ulcers did not vary between rural and urban patients but was higher in males than in females. Females also had lower odds of having respiratory events but there was no sex difference in history of painful crises. Persons with more severe genotypes lived in higher poverty and travelled longer for healthcare services. Persons in areas with higher annual rainfall, higher mean temperatures and living farther from factories had less painful crises and respiratory events. The paper highlights a need for better access to healthcare services for Jamaicans with SCD especially in rural areas of the island. It also reports interesting associations between environmental climatic exposures and health outcomes.

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<![CDATA[Antiretroviral Therapy Program Expansion in Zambézia Province, Mozambique: Geospatial Mapping of Community-Based and Health Facility Data for Integrated Health Planning]]> https://www.researchpad.co/article/5989d9ecab0ee8fa60b6ca55

Objective

To generate maps reflecting the intersection of community-based Voluntary Counseling and Testing (VCT) delivery points with facility-based HIV program demographic information collected at the district level in three districts (Ile, Maganja da Costa and Chinde) of Zambézia Province, Mozambique; in order to guide planning decisions about antiretroviral therapy (ART) program expansion.

Methods

Program information was harvested from two separate open source databases maintained for community-based VCT and facility-based HIV care and treatment monitoring from October 2011 to September 2012. Maps were created using ArcGIS 10.1. Travel distance by foot within a 10 km radius is generally considered a tolerable distance in Mozambique for purposes of adherence and retention planning.

Results

Community-based VCT activities in each of three districts were clustered within geographic proximity to clinics providing ART, within communities with easier transportation access, and/or near the homes of VCT volunteers. Community HIV testing results yielded HIV seropositivity rates in some regions that were incongruent with the Ministry of Health’s estimates for the entire district (2–13% vs. 2% in Ile, 2–54% vs. 11.5% in Maganja da Costa, and 23–43% vs. 14.4% in Chinde). All 3 districts revealed gaps in regional disbursement of community-based VCT activities as well as access to clinics offering ART.

Conclusions

Use of geospatial mapping in the context of program planning and monitoring allowed for characterizing the location and size of each district’s HIV population. In extremely resource limited and logistically challenging settings, maps are valuable tools for informing evidence-based decisions in planning program expansion, including ART.

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<![CDATA[Chronic Arsenic Exposure and Risk of Post Kala-azar Dermal Leishmaniasis Development in India: A Retrospective Cohort Study]]> https://www.researchpad.co/article/5989dae3ab0ee8fa60bbc9a2

Background

Visceral leishmaniasis (VL), with the squeal of Post-kala-azar dermal leishmaniasis (PKDL), is a global threat for health. Studies have shown sodium stibogluconate (SSG) resistance in VL patients with chronic arsenic exposure. Here, we assessed the association between arsenic exposure and risk of developing PKDL in treated VL patients.

Methods

In this retrospective study, PKDL patients (n = 139), earlier treated with SSG or any other drug during VL, were selected from the study cohort. Trained physicians, unaware of arsenic exposure, interviewed them and collected relevant data in a questionnaire format. All probable water sources were identified around the patient’s house and water was collected for evaluation of arsenic concentration. A GIS-based village-level digital database of PKDL cases and arsenic concentration in groundwater was developed and individual point location of PKDL cases were overlaid on an integrated GIS map. We used multivariate logistic regression analysis to assess odds ratios (ORs) for association between arsenic exposure and PKDL development.

Results

Out of the 429 water samples tested, 403 had arsenic content of over 10 μg/L, with highest level of 432 μg/L among the seven study villages. Multivariate adjusted ORs for risk of PKDL development in comparison of arsenic concentrations of 10.1–200 μg/L and 200.1–432.0 μg/L were 1.85 (1.13–3.03) and 2.31 (1.39–3.8) respectively. Interestingly, similar results were found for daily dose of arsenic and total arsenic concentration in urine sample of the individual. The multivariate-adjusted OR for comparison of high baseline arsenic exposure to low baseline arsenic exposure of the individuals in the study cohort was 1.66 (95% CI 1.02–2.7; p = 0.04).

Conclusion

Our findings indicate the need to consider environmental factors, like long time arsenic exposure, as an additional influence on treated VL patients towards risk of PKDL development in Bihar.

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<![CDATA[A Combined Approach to Cartographic Displacement for Buildings Based on Skeleton and Improved Elastic Beam Algorithm]]> https://www.researchpad.co/article/5989db3fab0ee8fa60bd5fd2

Scale reduction from source to target maps inevitably leads to conflicts of map symbols in cartography and geographic information systems (GIS). Displacement is one of the most important map generalization operators and it can be used to resolve the problems that arise from conflict among two or more map objects. In this paper, we propose a combined approach based on constraint Delaunay triangulation (CDT) skeleton and improved elastic beam algorithm for automated building displacement. In this approach, map data sets are first partitioned. Then the displacement operation is conducted in each partition as a cyclic and iterative process of conflict detection and resolution. In the iteration, the skeleton of the gap spaces is extracted using CDT. It then serves as an enhanced data model to detect conflicts and construct the proximity graph. Then, the proximity graph is adjusted using local grouping information. Under the action of forces derived from the detected conflicts, the proximity graph is deformed using the improved elastic beam algorithm. In this way, buildings are displaced to find an optimal compromise between related cartographic constraints. To validate this approach, two topographic map data sets (i.e., urban and suburban areas) were tested. The results were reasonable with respect to each constraint when the density of the map was not extremely high. In summary, the improvements include (1) an automated parameter-setting method for elastic beams, (2) explicit enforcement regarding the positional accuracy constraint, added by introducing drag forces, (3) preservation of local building groups through displacement over an adjusted proximity graph, and (4) an iterative strategy that is more likely to resolve the proximity conflicts than the one used in the existing elastic beam algorithm.

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<![CDATA[Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance]]> https://www.researchpad.co/article/5989dadfab0ee8fa60bbb577

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

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<![CDATA[A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems]]> https://www.researchpad.co/article/5989db29ab0ee8fa60bd0e91

A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client’s requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users’ access behaviors and all tiles’ relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users’ access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods.

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<![CDATA[Analysis of Feature Intervisibility and Cumulative Visibility Using GIS, Bayesian and Spatial Statistics: A Study from the Mandara Mountains, Northern Cameroon]]> https://www.researchpad.co/article/5989da5dab0ee8fa60b90414

The locations of diy-geδ-bay (DGB) sites in the Mandara Mountains, northern Cameroon are hypothesized to occur as a function of their ability to see and be seen from points on the surrounding landscape. A series of geostatistical, two-way and Bayesian logistic regression analyses were performed to test two hypotheses related to the intervisibility of the sites to one another and their visual prominence on the landscape. We determine that the intervisibility of the sites to one another is highly statistically significant when compared to 10 stratified-random permutations of DGB sites. Bayesian logistic regression additionally demonstrates that the visibility of the sites to points on the surrounding landscape is statistically significant. The location of sites appears to have also been selected on the basis of lower slope than random permutations of sites. Using statistical measures, many of which are not commonly employed in archaeological research, to evaluate aspects of visibility on the landscape, we conclude that the placement of DGB sites improved their conspicuousness for enhanced ritual, social cooperation and/or competition purposes.

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<![CDATA[Road Development and the Geography of Hunting by an Amazonian Indigenous Group: Consequences for Wildlife Conservation]]> https://www.researchpad.co/article/5989da06ab0ee8fa60b75f43

Protected areas are essential for conservation of wildlife populations. However, in the tropics there are two important factors that may interact to threaten this objective: 1) road development associated with large-scale resource extraction near or within protected areas; and 2) historical occupancy by traditional or indigenous groups that depend on wildlife for their survival. To manage wildlife populations in the tropics, it is critical to understand the effects of roads on the spatial extent of hunting and how wildlife is used. A geographical analysis can help us answer questions such as: How do roads affect spatial extent of hunting? How does market vicinity relate to local consumption and trade of bushmeat? How does vicinity to markets influence choice of game? A geographical analysis also can help evaluate the consequences of increased accessibility in landscapes that function as source-sink systems. We applied spatial analyses to evaluate the effects of increased landscape and market accessibility by road development on spatial extent of harvested areas and wildlife use by indigenous hunters. Our study was conducted in Yasuní Biosphere Reserve, Ecuador, which is impacted by road development for oil extraction, and inhabited by the Waorani indigenous group. Hunting activities were self-reported for 12–14 months and each kill was georeferenced. Presence of roads was associated with a two-fold increase of the extraction area. Rates of bushmeat extraction and trade were higher closer to markets than further away. Hunters located closer to markets concentrated their effort on large-bodied species. Our results clearly demonstrate that placing roads within protected areas can seriously reduce their capacity to sustain wildlife populations and potentially threaten livelihoods of indigenous groups who depend on these resources for their survival. Our results critically inform current policy debates regarding resource extraction and road building near or within protected areas.

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<![CDATA[Quantitative, Qualitative and Geospatial Methods to Characterize HIV Risk Environments]]> https://www.researchpad.co/article/5989db49ab0ee8fa60bd9711

Increasingly, ‘place’, including physical and geographical characteristics as well as social meanings, is recognized as an important factor driving individual and community health risks. This is especially true among marginalized populations in low and middle income countries (LMIC), whose environments may also be more difficult to study using traditional methods. In the NIH-funded longitudinal study Mapa de Salud, we employed a novel approach to exploring the risk environment of female sex workers (FSWs) in two Mexico/U.S. border cities, Tijuana and Ciudad Juárez. In this paper we describe the development, implementation, and feasibility of a mix of quantitative and qualitative tools used to capture the HIV risk environments of FSWs in an LMIC setting. The methods were: 1) Participatory mapping; 2) Quantitative interviews; 3) Sex work venue field observation; 4) Time-location-activity diaries; 5) In-depth interviews about daily activity spaces. We found that the mixed-methodology outlined was both feasible to implement and acceptable to participants. These methods can generate geospatial data to assess the role of the environment on drug and sexual risk behaviors among high risk populations. Additionally, the adaptation of existing methods for marginalized populations in resource constrained contexts provides new opportunities for informing public health interventions.

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<![CDATA[A Secure Region-Based Geographic Routing Protocol (SRBGR) for Wireless Sensor Networks]]> https://www.researchpad.co/article/5989db52ab0ee8fa60bdc8b3

Due to the lack of dependency for routing initiation and an inadequate allocated sextant on responding messages, the secure geographic routing protocols for Wireless Sensor Networks (WSNs) have attracted considerable attention. However, the existing protocols are more likely to drop packets when legitimate nodes fail to respond to the routing initiation messages while attackers in the allocated sextant manage to respond. Furthermore, these protocols are designed with inefficient collection window and inadequate verification criteria which may lead to a high number of attacker selections. To prevent the failure to find an appropriate relay node and undesirable packet retransmission, this paper presents Secure Region-Based Geographic Routing Protocol (SRBGR) to increase the probability of selecting the appropriate relay node. By extending the allocated sextant and applying different message contention priorities more legitimate nodes can be admitted in the routing process. Moreover, the paper also proposed the bound collection window for a sufficient collection time and verification cost for both attacker identification and isolation. Extensive simulation experiments have been performed to evaluate the performance of the proposed protocol in comparison with other existing protocols. The results demonstrate that SRBGR increases network performance in terms of the packet delivery ratio and isolates attacks such as Sybil and Black hole.

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<![CDATA[Global Disease Monitoring and Forecasting with Wikipedia]]> https://www.researchpad.co/article/5989da50ab0ee8fa60b8dc9c

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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<![CDATA[Inter-Coder Agreement in One-to-Many Classification: Fuzzy Kappa]]> https://www.researchpad.co/article/5989da74ab0ee8fa60b95d6a

Content analysis involves classification of textual, visual, or audio data. The inter-coder agreement is estimated by making two or more coders to classify the same data units, with subsequent comparison of their results. The existing methods of agreement estimation, e.g., Cohen’s kappa, require that coders place each unit of content into one and only one category (one-to-one coding) from the pre-established set of categories. However, in certain data domains (e.g., maps, photographs, databases of texts and images), this requirement seems overly restrictive. The restriction could be lifted, provided that there is a measure to calculate the inter-coder agreement in the one-to-many protocol. Building on the existing approaches to one-to-many coding in geography and biomedicine, such measure, fuzzy kappa, which is an extension of Cohen’s kappa, is proposed. It is argued that the measure is especially compatible with data from certain domains, when holistic reasoning of human coders is utilized in order to describe the data and access the meaning of communication.

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<![CDATA[Comprehensive framework for visualizing and analyzing spatio-temporal dynamics of racial diversity in the entire United States]]> https://www.researchpad.co/article/5989db52ab0ee8fa60bdc75f

The United States is increasingly becoming a multi-racial society. To understand multiple consequences of this overall trend to our neighborhoods we need a methodology capable of spatio-temporal analysis of racial diversity at the local level but also across the entire U.S. Furthermore, such methodology should be accessible to stakeholders ranging from analysts to decision makers. In this paper we present a comprehensive framework for visualizing and analyzing diversity data that fulfills such requirements. The first component of our framework is a U.S.-wide, multi-year database of race sub-population grids which is freely available for download. These 30 m resolution grids have being developed using dasymetric modeling and are available for 1990-2000-2010. We summarize numerous advantages of gridded population data over commonly used Census tract-aggregated data. Using these grids frees analysts from constructing their own and allows them to focus on diversity analysis. The second component of our framework is a set of U.S.-wide, multi-year diversity maps at 30 m resolution. A diversity map is our product that classifies the gridded population into 39 communities based on their degrees of diversity, dominant race, and population density. It provides spatial information on diversity in a single, easy-to-understand map that can be utilized by analysts and end users alike. Maps based on subsequent Censuses provide information about spatio-temporal dynamics of diversity. Diversity maps are accessible through the GeoWeb application SocScape (http://sil.uc.edu/webapps/socscape_usa/) for an immediate online exploration. The third component of our framework is a proposal to quantitatively analyze diversity maps using a set of landscape metrics. Because of its form, a grid-based diversity map could be thought of as a diversity “landscape” and analyzed quantitatively using landscape metrics. We give a brief summary of most pertinent metrics and demonstrate how they can be applied to diversity maps.

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<![CDATA[Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands]]> https://www.researchpad.co/article/5989da96ab0ee8fa60ba1cde

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach.

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<![CDATA[Socioeconomic Position and Low Birth Weight among Mothers Exposed to Traffic-Related Air Pollution]]> https://www.researchpad.co/article/5989dacbab0ee8fa60bb4148

Background

Atmospheric pollution is a major public health concern. It can affect placental function and restricts fetal growth. However, scientific knowledge remains too limited to make inferences regarding causal associations between maternal exposure to air pollution and adverse effects on pregnancy. This study evaluated the association between low birth weight (LBW) and maternal exposure during pregnancy to traffic related air pollutants (TRAP) in São Paulo, Brazil.

Methods and findings

Analysis included 5,772 cases of term-LBW (<2,500 g) and 5,814 controls matched by sex and month of birth selected from the birth registration system. Mothers’ addresses were geocoded to estimate exposure according to 3 indicators: distance from home to heavy traffic roads, distance-weighted traffic density (DWTD) and levels of particulate matter ≤10 µg/m3 estimated through land use regression (LUR-PM10). Final models were evaluated using multiple logistic regression adjusting for birth, maternal and pregnancy characteristics. We found decreased odds in the risk of LBW associated with DWTD and LUR-PM10 in the highest quartiles of exposure with a significant linear trend of decrease in risk. The analysis with distance from heavy traffic roads was less consistent. It was also observed that mothers with higher education and neighborhood-level income were potentially more exposed to TRAP.

Conclusions

This study found an unexpected decreased risk of LBW associated with traffic related air pollution. Mothers with advantaged socioeconomic position (SEP) although residing in areas of higher vehicular traffic might not in fact be more expose to air pollution. It can also be that the protection against LBW arising from a better SEP is stronger than the effect of exposure to air pollution, and this exposure may not be sufficient to increase the risk of LBW for these mothers.

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<![CDATA[Climate Change Sensitivity Index for Pacific Salmon Habitat in Southeast Alaska]]> https://www.researchpad.co/article/5989da8dab0ee8fa60b9e913

Global climate change may become one of the most pressing challenges to Pacific Salmon conservation and management for southeast Alaska in the 21st Century. Predicted hydrologic change associated with climate change will likely challenge the ability of specific stocks to adapt to new flow regimes and resulting shifts in spawning and rearing habitats. Current research suggests egg-to-fry survival may be one of the most important freshwater limiting factors in Pacific Salmon's northern range due to more frequent flooding events predicted to scour eggs from mobile spawning substrates. A watershed-scale hydroclimatic sensitivity index was developed to map this hypothesis with an historical stream gauge station dataset and monthly multiple regression-based discharge models. The relative change from present to future watershed conditions predicted for the spawning and incubation period (September to March) was quantified using an ensemble global climate model average (ECHAM5, HadCM3, and CGCM3.1) and three global greenhouse gas emission scenarios (B1, A1B, and A2) projected to the year 2080. The models showed the region's diverse physiography and climatology resulted in a relatively predictable pattern of change: northern mainland and steeper, snow-fed mountainous watersheds exhibited the greatest increases in discharge, an earlier spring melt, and a transition into rain-fed hydrologic patterns. Predicted streamflow increases for all watersheds ranged from approximately 1-fold to 3-fold for the spawning and incubation period, with increased peak flows in the spring and fall. The hydroclimatic sensitivity index was then combined with an index of currently mapped salmon habitat and species diversity to develop a research and conservation priority matrix, highlighting potentially vulnerable to resilient high-value watersheds. The resulting matrix and observed trends are put forth as a framework to prioritize long-term monitoring plans, mitigation experiments, and finer-scale climate impact and adaptation studies.

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