Spatial Analysis of Chinese Grain Production for Sustainable Land Management in Plain, Hill, and Mountain Counties

Sustainability 2017, 9(3), 348

By Jinlang Zou and Qun Wu

“In the context of China’s food security, spatially explicit information on grain production is an important asset to achieve the sustainable management of cultivated land. Previous studies have shown that spatial mismatches exist between grain production and water and cultivated land resources. In this paper, county-level data are used to investigate the degree of spatial (mis)match between grain output and the geographical distribution patterns of plain, hill, and mountain counties.

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County-level dominant landform classification into plain counties, hill counties, or mountain counties based on the China County Statistical Yearbook.

“We estimate the difference in grain output between these different types of counties with a Spatial Autoregression Model. The results indicate that plain counties have the highest grain output, followed by hill counties and mountain counties subsequently. The reasons for the higher production in plain counties lie in the presence of more cultivated land, as well as a higher degree of irrigation and agricultural mechanization. The current pattern of Chinese total grain production follows the law of substituting labor with mechanization. Improving efficiency in the use of water resources and chemical fertilizer is both urgent and crucial. In this paper, we propose that the future roles for total grain production in relation to landforms should be: increased production and competitiveness in plain counties, a stabilization of capacity in hill counties, and a decrease in grain production in mountain counties.”

Neighbourhood Walkability and Daily Steps in Adults with Type 2 Diabetes

PLOSone, published online March 18, 2016

By Samantha Hajna, Nancy A. Ross, Lawrence Joseph, Sam Harper, and Kaberi Dasgupta

“There is evidence that greater neighbourhood walkability (i.e., neighbourhoods with more amenities and well-connected streets) is associated with higher levels of total walking in Europe and in Asia, but it remains unclear if this association holds in the Canadian context and in chronic disease populations. We examined the relationships of different walkability measures to biosensor-assessed total walking (i.e., steps/day) in adults with type 2 diabetes living in Montreal (QC, Canada).

Materials and Methods

Participants (60.5±10.4 years; 48.1% women) were recruited through McGill University-affiliated clinics (June 2006 to May 2008). Steps/day were assessed once per season for one year with pedometers. Neighbourhood walkability was evaluated through participant reports, in-field audits, Geographic Information Systems (GIS)-derived measures, and the Walk Score®. Relationships between walkability and daily steps were estimated using Bayesian longitudinal hierarchical linear regression models (n = 131).

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Results

Participants who reported living in the most compared to the least walkable neighbourhoods completed 1345 more steps/day (95% Credible Interval: 718, 1976; Quartiles 4 versus 1). Those living in the most compared to the least walkable neighbourhoods (based on GIS-derived walkability) completed 606 more steps per day (95% CrI: 8, 1203). No statistically significant associations with steps were observed for audit-assessed walkability or the Walk Score®.

Conclusions

Adults with type 2 diabetes who perceived their neighbourhoods as more walkable accumulated more daily steps. This suggests that knowledge of local neighborhood features that enhance walking is a meaningful predictor of higher levels of walking and an important component of neighbourhood walkability.”

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

PLOSone, published online May 20, 2015

By M. Ali Jalali, Daniel Ierodiaconou, Harry Gorfine, Jacquomo Monk, and Alex Rattray

“Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area.

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“Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.”

Spatial Access to Emergency Services in Low- and Middle-Income Countries: A GIS-Based Analysis

PLOSone, published online November 3, 2015

By Gavin Tansley, Nadine Schuurman, Ofer Amram, and Natalie Yanchar

“Injury is a leading cause of the global disease burden, accounting for 10 percent of all deaths worldwide. Despite 90 percent of these deaths occurring in low and middle-income countries (LMICs), the majority of trauma research and infrastructure development has taken place in high-income settings. Furthermore, although accessible services are of central importance to a mature trauma system, there remains a paucity of literature describing the spatial accessibility of emergency services in LMICs. Using data from the Service Provision Assessment component of the Demographic and Health Surveys of Namibia and Haiti we defined the capabilities of healthcare facilities in each country in terms of their preparedness to provide emergency services. A Geographic Information System-based network analysis method was used to define 5- 10- and 50-kilometer catchment areas for all facilities capable of providing 24-hour care, higher-level resuscitative services or tertiary care.

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“The proportion of a country’s population with access to each level of service was obtained by amalgamating the catchment areas with a population layer. A significant proportion of the population of both countries had poor spatial access to lower level services with 25% of the population of Haiti and 51% of the population of Namibia living further than 50 kilometers from a facility capable of providing 24-hour care. Spatial access to tertiary care was considerably lower with 51% of Haitians and 72% of Namibians having no access to these higher-level services within 50 kilometers. These results demonstrate a significant disparity in potential spatial access to emergency services in two LMICs compared to analogous estimates from high-income settings, and suggest that strengthening the capabilities of existing facilities may improve the equity of emergency services in these countries. Routine collection of georeferenced patient and facility data in LMICs will be important to understanding how spatial access to services influences outcomes.”

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

PLOSone, published online July 25, 2016

By Chris Allen, Ming-Hsiang Tsou, Anoshe Aslam, Anna Nagel, and Jean-Mark Gawron

“Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013–2014 flu season.

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“The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.”

Spatial Analysis of Dengue Seroprevalence and Modeling of Transmission Risk Factors in a Dengue Hyperendemic City of Venezuela

ntdPLOS | Neglected Tropical Diseases, Published Online 23 January 2017

By Maria F. Vincenti-Gonzalez, María-Eugenia Grillet, Zoraida I. Velasco-Salas, Erley F. Lizarazo, Manuel A. Amarista, Gloria M. Sierra, Guillermo Comach, and Adriana Tami

Background
“Dengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach.

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Dengue seroprevalence within Candelaria & Caña de Azúcar neighborhoods, Maracay city, Venezuela.

Methodology/Principal Findings

“From August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela. Local spatial statistics conducted at household- and block-level identified clusters of recent dengue seroprevalence (39 hot spot households and 9 hot spot blocks) in all neighborhoods. However, no clusters were found for past dengue seroprevalence. Clustering of infection was detected at a very small scale (20-110m) suggesting a high disease focal aggregation. Factors associated with living in a hot spot household were occupation (being a domestic worker/housewife (P = 0.002), lower socio-economic status (living in a shack (P<0.001), sharing a household with <7 people (P = 0.004), promoting potential vector breeding sites (storing water in containers (P = 0.024), having litter outdoors (P = 0.002) and mosquito preventive measures (such as using repellent, P = 0.011). Similarly, low socio-economic status (living in crowded conditions, P<0.001), having an occupation of domestic worker/housewife (P = 0.012) and not using certain preventive measures against mosquitoes (P<0.05) were directly associated with living in a hot spot block.

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Dengue seroprevalence within La Cooperativa neighborhood, Maracay city, Venezuela.

Conclusions/Significance
“Our findings contribute to a better comprehension of the spatial dynamics of dengue by assessing the relationship between disease clusters and their risk factors. These results can inform health authorities in the design of surveillance and control activities. Focalizing dengue control measures during epidemic and inter-epidemic periods to disease high risk zones at household and neighborhood-level may significantly reduce virus transmission in comparison to random interventions.”

Zika virus: Endemic and epidemic ranges of Aedes mosquito transmission

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Journal of Infection and Public Health, Volume 10, Issue 1, January–February 2017, Pages 120–123

By David F. Attaway, Nigel M. Waters, Estella M. Geraghty, and Kathryn H. Jacobsen

“As evidence linking Zika virus with serious health complications strengthens, public health officials and clinicians worldwide need to know which locations are likely to be at risk for autochthonous Zika infections. We created risk maps for epidemic and endemic Aedes-borne Zika virus infections globally using a predictive analysis method that draws on temperature, precipitation, elevation, land cover, and population density variables to identify locations suitable for mosquito activity seasonally or year-round.
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Global map of the locations suitable for seasonal presence (yellow) and year-round presence (red) of hematophagous Aedes mosquitoes.

Aedes mosquitoes capable of transmitting Zika and other viruses are likely to live year-round across many tropical areas in the Americas, Africa, and Asia. Our map provides an enhanced global projection of where vector control initiatives may be most valuable for reducing the risk of Zika virus and other Aedes-borne infections.”