Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture.

Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components. In this research, two studies were carried out to determine the reliability of the data obtained by different SEnviro nodes installed in vineyards. Two networks of meteorological stations were used to carry out these studies, one official and the other professional. The first study was based on calculating the homogenisation of the data, which was performed using the Climatol tool. The second study proposed a similarity analysis using cross-correlation. The results showed that the low-cost node can be used to monitor climatic conditions in an agricultural area in the central zone of the province of Castelló (Spain) and to obtain reliable observations for use in previously published fungal disease models.

Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard.

Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996-2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984-2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.

Understanding the determinants of GIT post-adoption: perspectives from Mozambican institutions.

This study aims to understand the determinants of geographical information technologies at the scale of post-adoption use and intention to increase the level of use in Mozambican institutions. Three known theories (diffusion of innovation theory, technology - organisation - environment framework, and institutional theory) have been used in order to accomplish the study. The data analysis showed that the variables compatibility, geographical scope, expansion opportunities, and normative pressure contribute to explaining GIT use. Relative advantage, complexity, coercive pressure, and mimetic pressure contribute to explaining the intention to increase GIT levels of use. The model revealed substantial power of explanation for GIT post-adoption.

Corrigendum to "Place and city: toward a geography of engagement" [Heliyon 5 (8) (August 2019) e02261].

[This corrects the article DOI: 10.1016/j.heliyon.2019.e02261.].

Place and city: toward a geography of engagement.

The relationship between sense of place, social capital and civic engagement has been studied in different disciplines. However, their association has been less examined, and their spatial relationship has been analyzed even less. This study contributes to a better understanding of the relationship between these three concepts (i.e., sense of place, social capital and civic engagement). Furthermore, we analyze the crucial role that the spatial relationship between them plays. Using spatial data collected through a web map-based application, we adopt structural equation modeling (SEM) techniques to assess the repercussion that sense of place has on social capital and how the latter affects civic engagement. We find that sense of place is significant and positively correlated with social capital, while the latter also significantly explains civic engagement at the individual level. Furthermore, we observe a better statistical performance in almost all cases when a spatial relationship between the three constructs exists. Our research leverages SEM techniques, Geographic Information Science (GISc) methods, and participatory methodology to show the spatial connection between sense of place and social capital to explain civic engagement. Deriving and quantifying such meaning allows us to highlight the importance of their spatial dimension in city processes such as participation.

A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal.

The risk of developing lung cancer might to a certain extent be attributed to tobacco. Nevertheless, the role of air pollution, both form urban and industrial sources, needs to be addressed. Numerous studies have concluded that long-term exposure to air pollution is an important environmental risk factor for lung cancer mortality. Still, there are only a few studies on air pollution and lung cancer in Portugal and none addressing its spatial dimension. The goal was to determine the influence of air pollution and urbanization rate on lung cancer mortality. A geographically weighted regression (GWR) model was performed to evaluate the relation between particle matter10 (PM10) emissions and lung cancer mortality relative risk (RR) for males and females in Portugal between 2007 and 2011. RR was computed with the BYM model. For a more in-depth analysis, the urbanization rate and the percentage of industrial area in each municipality were added. GWR efforts led to identifying three variables that were statistically significant in explaining lung cancer relative risk mortality, PM10 emissions, urbanization rate and the percentage of industrial area with an adjusted R2 of 0,63 for men and 0,59 for women. A small set of 8 municipalities with high correlation values was also identified (local R2 above 0,70). Stronger relationships were found in the north-western part of mainland Portugal. The local R2 tends to be higher when the emissions of PM10 are joined by urbanization and industrial areas. However, when assessing the industrial areas alone, it was noted that its impact was lower overall. As one of the first communications on this subject in Portugal, we have identified municipalities where possible impacts of air pollution on lung cancer mortality RR are higher thereby highlighting the role of geography and spatial analysis in explaining the associations between a disease and its determinants.

Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique.

Background: Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods: A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results: Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions: The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

The relevance of spatial aggregation level and of applied methods in the analysis of geographical distribution of cancer mortality in mainland Portugal (2009-2013).

BACKGROUND: Knowledge regarding the geographical distribution of diseases is essential in public health in order to define strategies to improve the health of populations and quality of life. The present study aims to establish a methodology to choose a suitable geographic aggregation level of data and an appropriated method which allow us to analyze disease spatial patterns in mainland Portugal, avoiding the "small numbers problem." Malignant cancer mortality data for 2009-2013 was used as a case study. METHODS: To achieve our aims, we used official data regarding the mortality by all malignant cancer, between 2009 and 2013, and the mainland Portuguese resident population in 2011. Three different spatial aggregation levels were applied: Nomenclature of Territorial Units for Statistics, level III (28 areas), municipalities (278 areas), and parishes (4050 areas). Standardized Mortality Ratio (SMR) and relative risk (RR) were computed with Besag, York and Mollié model (BYM) for the evaluation of geographic patterns of mortality data. We also estimated Global Moran's I, Local Moran's I, and posterior probability (PP) for the spatial cluster analysis. RESULTS: Our results show that the occurrence of lower and higher extreme values of the standardized mortality ratio tend to increase with the decrease of data spatial aggregation. In addition, the number of local clusters is higher at small spatial aggregation levels, although the area of each cluster is generally smaller. Regarding global clustering, data forms clusters at all considered levels. Relative risk (RR) computed by Besag, York and Mollié model, in turn, also shows different results at the municipalities and parishes levels. However, the difference is smaller than the difference obtained by SMR computation. This statement is supported by the coefficient variation values. CONCLUSIONS: Our findings show that the choice of spatial data aggregation level has high importance in the research results, as different aggregation levels can lead to distinct results. In terms of the case study, we conclude that for the period of 2009-2013, cancer mortality in mainland Portugal formed clusters. The most suitable applicable spatial scale and method seemed to be at the municipalities level and Besag, York and Mollié model, respectively. However, further studies should be conducted in order to provide greater support to these results.

Malaria mortality characterization and the relationship between malaria mortality and climate in Chimoio, Mozambique.

BACKGROUND: The United Nation's sustainable development goal for 2030 is to eradicate the global malaria epidemic, primarily as the disease continues to be one of the major concerns for public health in sub-Saharan Africa. In 2015, the region accounted for 90% of malaria deaths. Mozambique recorded a malaria mortality rate of 42.75 (per 100,000). In Chimoio, Mozambique's fifth largest city, malaria is the fourth leading cause of death (9.4%). Few data on malaria mortality exists in Mozambique, particularly in relation to Chimoio. The objective of this study was to characterize malaria mortality trends and its spatial distribution in Chimoio. METHODS: Malaria mortality data and climate data were extracted from the Chimoio Civil Registration records, and the Regional Weather station, from 2010 to 2014. The malaria crude mortality rate was calculated. ANOVA, Tukey's, Chi square, and time series were carried out and an intervention analysis ARIMA model developed. RESULTS: A total of 944 malaria death cases were registered in Chimoio, 729 of these among Chimoio residents (77%). The average malaria mortality by gender was 44.9% for females and 55.1% for males. The age of death varied from 0 to 96 years, with an average age of 25.9 (SE = 0.79) years old. January presented the highest average of malaria deaths, and urban areas presented a lower crude malaria mortality rate. Rural neighbourhoods with good accessibility present the highest malaria crude mortality rate, over 85 per 100,000. Seasonal ARMA (2,0)(1,0)12 fitted the data although it was not able to capture malaria mortality peaks occurring during malaria outbreaks. Intervention effect properly fit the mortality peaks and reduced ARMA's root mean square error by almost 25%. CONCLUSION: Malaria mortality is increasing in Chimoio; children between 1 and 4 years old represent 13% of Chimoio population, but account for 25% of malaria mortality. Malaria mortality shows seasonal and spatial characteristics. More studies should be carried out for malaria eradication in the municipality.

Spatial epidemiology of cancer: a review of data sources, methods and risk factors.

Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i) analysis of spatial distribution of cancer and/or its temporal evolution; ii) risk factors; iii) development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer's spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations.

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique.

BACKGROUND: Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication. METHODS: Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model. RESULTS: Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)52, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately. CONCLUSION: The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

Spatio-temporal variation and socio-demographic characters of malaria in Chimoio municipality, Mozambique.

BACKGROUND: In Africa, urban malaria is a major concern, since the towns and especially their suburbs are growing quickly. In Mozambique, malaria represents 45 % of all cases of outpatient visits and 56 % of inpatient visits at paediatric clinics. Malaria is a major public health burden in Chimoio Mozambique and few studies on malaria exist. METHODS: The study was carried out to establish the spatiality and temporality of malaria and describe socio-demographic characteristics of malaria patients in Chimoio. Weekly malaria data for 9 years (2006-2014) were collected from the district Epidemiological Bulletin and incidence by season, age, gender, and residence was calculated. SPSS version 20 was used for statistical analysis and ArcGis 10.1 was used to produce maps. RESULTS: The annual overall average of malaria incidence was 20.1 % and the attributable fraction (AF) of malaria was 16 %. There were differences in weekly and yearly malaria occurrences throughout the period. There was no difference in malaria cases between male and female patients. Children under 5 years of age are three times more prone to malaria than adults (p < 0.05). Three temporal clusters of malaria were identified: cluster 1, weeks 25-47 with average weekly cases of 618 (sd = 251.9), cluster 2, weeks 18-24 and 48-51 with average weekly cases of 1066 (sd = 317.4). cluster 3, weeks 1-17 and 52 with average weekly cases of 1587 (sd = 722.4). Similarly, three different clusters were identified according to residential areas: cluster 1 (10 %) mostly urban, cluster 2 (22 %) mostly suburbs, cluster 3 (28 %) mostly rural areas. CONCLUSION: Malaria is increasing in the suburbs, and rural areas present more cases of malaria compared to urban areas. This article is an initial step to understand the dynamics of malaria in Chimoio. Results suggest that malaria varies in time and space, and that precision public health strategy should be used to control malaria occurrence. Studies on weather factors affecting malaria cases, bed net usage, and others should be undertaken.

A topological multilayer model of the human body.

Geographical information systems deal with spatial databases in which topological models are described with alphanumeric information. Its graphical interfaces implement the multilayer concept and provide powerful interaction tools. In this study, we apply these concepts to the human body creating a representation that would allow an interactive, precise, and detailed anatomical study. A vector surface component of the human body is built using a three-dimensional (3-D) reconstruction methodology. This multilayer concept is implemented by associating raster components with the corresponding vector surfaces, which include neighbourhood topology enabling spatial analysis. A root mean square error of 0.18 mm validated the three-dimensional reconstruction technique of internal anatomical structures. The expansion of the identification and the development of a neighbourhood analysis function are the new tools provided in this model.

Ecological risk assessment of sediment management areas: application to Sado Estuary, Portugal.

The purpose of this work was to integrate different methodologies to assess the potential ecological risk of estuarine sedimentary management areas, using the Sado Estuary in Portugal as case study. To evaluate the environmental risk of sediment contamination, an integrative and innovative approach was used involving assessment of sediment chemistry, sediment toxicity, benthic community structure, human driving forces and pressures and management areas organic load levels. The basis for decision-making for overall assessment was a statistical multivariate analysis appended into a score matrix tables, using a best expert judgment. The integrated approach allowed to identify from the 19 management areas analyzed, three with no risk but other three with high risk to cause adverse effects in the biota, related with the contaminants analyzed. The methodologies used showed to be effective as a support for decision making leading to future estuarine management recommendations.

Delineation of estuarine management areas using multivariate geostatistics: the case of Sado Estuary.

The Sado Estuary is a coastal zone located in the south of Portugal where conflicts between conservation and development exist because of its location near industrialized urban zones and its designation as a natural reserve. The aim of this paper is to evaluate a set of multivariate geostatistical approaches to delineate spatially contiguous regions of sediment structure for Sado Estuary. These areas will be the supporting infrastructure of an environmental management system for this estuary. The boundaries of each homogeneous area were derived from three sediment characterization attributes through three different approaches: (1) cluster analysis of dissimilarity matrix function of geographical separation followed by indicator kriging of the cluster data, (2) discriminant analysis of kriged values of the three sediment attributes, and (3) a combination of methods 1 and 2. Final maximum likelihood classification was integrated into a geographical information system. All methods generated fairly spatially contiguous management areas that reproduce well the environment of the estuary. Map comparison techniques based on kappa statistics showed thatthe resultant three maps are similar, supporting the choice of any of the methods as appropriate for management of the Sado Estuary. However, the results of method 1 seem to be in better agreement with estuary behavior, assessment of contamination sources, and previous work conducted at this site.