Browsing by Subject "Spatial modelling"

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    Current patterns of habitat transformation and future threats to biodiversity in terrestrial ecosystems of the Cape Floristic Region, South Africa
    (2003) Rouget, Mathieu; Richardson, David M; Cowling, Richard M; Lloyd, J Wendy; Lombard, Amanda T
    The formulation of an effective strategic plan for biodiversity conservation in the Cape Floristic Region (CFR) requires an assessment of the current situation with regard to habitat transformation, and an explicit framework for predicting the likelihood of remaining habitat (i.e. that potentially available for conservation) being transformed. This paper presents the results of a detailed assessment of the current and future extent of three important factors that threaten biodiversity in the CFR: cultivation for intensive agriculture (including commercial forestry plantations), urbanisation, and stands of invasive (self-sown) alien trees and shrubs. The extent of habitat transformation was mapped at the scale of 1:250,000, using primarily satellite imagery. We compared models derived from a rule-based approach relying on expert knowledge and a regression-tree technique to identify other areas likely to be affected by these factors in future. Cultivation for agriculture has transformed 25.9% of the CFR and dense stands of woody alien plants and urban areas each cover 1.6%. Both models predict that at least 30% of the currently remaining natural vegetation could be transformed within 20 years. There was an overall accuracy of 73% between both models although significant differences were found for some habitat types. Spatial predictions of future agriculture threats derived from the rule-based approach were overestimated relative to the statistical approach, whereas future alien spread was underestimated. Threat assessment was used to derive conservation targets for subsequent stages of conservation planning for the CFR. The importance of integrating vulnerability knowledge into conservation planning is discussed. The choice of vulnerability analysis (future habitat degradation and/or impact on biological entities) and methods will depend on the complexity of the threatening processes and the availability of spatial data.
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    Spatial models for the rational allocation of routinely distributed bed nets to public health facilities in Western Kenya
    (2017) Macharia, Peter M; Odera, Patroba A; Snow, Robert W; Noor, Abdisalan M
    BACKGROUND: In high to moderate malaria transmission areas of Kenya, long-lasting insecticidal nets (LLINs) are provided free of charge to pregnant women and infants during routine antenatal care (ANC) and immunization respectively. Quantities of LLINs distributed to clinics are quantified based on a combination of monthly consumption data and population size of target counties. However, this approach has been shown to lead to stock-outs in targeted clinics. In this study, a novel LLINs need quantification approach for clinics in the routine distribution system was developed. The estimated need was then compared to the actual allocation to identify potential areas of LLIN over- or under-allocation in the high malaria transmission areas of Western Kenya. METHODS: A geocoded database of public health facilities was developed and linked to monthly LLIN allocation. A network analysis approach was implemented using the location of all public clinics and topographic layers to model travel time. Estimated travel time, socio-economic and ANC attendance data were used to model clinic catchment areas and the probability of ANC service use within these catchments. These were used to define the number of catchment population who were likely to use these clinics for the year 2015 equivalent to LLIN need. Actual LLIN allocation was compared with the estimated need. Clinics were then classified based on whether allocation matched with the need, and if not, whether they were over or under-allocated. RESULTS: 888 (70%) public health facilities were allocated 591,880 LLINs in 2015. Approximately 682,377 (93%) pregnant women and infants were likely to have attended an LLIN clinic. 36% of the clinics had more LLIN than was needed (over-allocated) while 43% had received less (under-allocated). Increasing efficiency of allocation by diverting over supply of LLIN to clinics with less stock and fully covering 43 clinics that did not receive nets in 2015 would allow for complete matching of need with distribution. CONCLUSION: The proposed spatial modelling framework presents a rationale for equitable allocation of routine LLINs and could be used for quantification of other maternal and child health commodities applicable in different settings. Western Kenya region received adequate LLINs for routine distribution in line with government of Kenya targets, however, the model shows important inefficiencies in the allocation of the LLINs at clinic level.
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    Spatial models for the rational allocation of routinely distributed bed nets to public health facilities in Western Kenya
    (BioMed Central, 2017-09-12) Macharia, Peter M; Odera, Patroba A; Snow, Robert W; Noor, Abdisalan M
    Background: In high to moderate malaria transmission areas of Kenya, long-lasting insecticidal nets (LLINs) are provided free of charge to pregnant women and infants during routine antenatal care (ANC) and immunization respectively. Quantities of LLINs distributed to clinics are quantified based on a combination of monthly consumption data and population size of target counties. However, this approach has been shown to lead to stock-outs in targeted clinics. In this study, a novel LLINs need quantification approach for clinics in the routine distribution system was developed. The estimated need was then compared to the actual allocation to identify potential areas of LLIN over- or under-allocation in the high malaria transmission areas of Western Kenya. Methods: A geocoded database of public health facilities was developed and linked to monthly LLIN allocation. A network analysis approach was implemented using the location of all public clinics and topographic layers to model travel time. Estimated travel time, socio-economic and ANC attendance data were used to model clinic catchment areas and the probability of ANC service use within these catchments. These were used to define the number of catchment population who were likely to use these clinics for the year 2015 equivalent to LLIN need. Actual LLIN allocation was compared with the estimated need. Clinics were then classified based on whether allocation matched with the need, and if not, whether they were over or under-allocated. Results: 888 (70%) public health facilities were allocated 591,880 LLINs in 2015. Approximately 682,377 (93%) pregnant women and infants were likely to have attended an LLIN clinic. 36% of the clinics had more LLIN than was needed (over-allocated) while 43% had received less (under-allocated). Increasing efficiency of allocation by diverting over supply of LLIN to clinics with less stock and fully covering 43 clinics that did not receive nets in 2015 would allow for complete matching of need with distribution. Conclusion: The proposed spatial modelling framework presents a rationale for equitable allocation of routine LLINs and could be used for quantification of other maternal and child health commodities applicable in different settings. Western Kenya region received adequate LLINs for routine distribution in line with government of Kenya targets, however, the model shows important inefficiencies in the allocation of the LLINs at clinic level.