Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017

Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Background: Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centers (HCs). Methods: Case data for 27 villages were collected in 13 HCs using continuous passive case detection. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Results: Overall, the incidence rate in the area was 429.13 cases per 1,000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1,000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1,750.75 cases per 1,000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The epidemic year was characterized by a major peak during the second part of the rainy season and a secondary peak during the dry-hot season. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables and associated with the first peak of cases) and 16 weeks for SMI2 (positively correlated with temperature variables and associated with the secondary peak of cases). Euclidian distance to HC was not found to be a predictor of malaria cases recorded in HC. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. Conclusions: Our spatio-temporal analysis of malaria cases in Diebougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns. Keywords Geo-epidemiology, Spatial Clusters, temporal dynamics, nonlinear relationship, prediction.

Coastal Tourism Spatial Planning at the Regional Unit: Identifying Coastal Tourism Hotspots Based on Social Media Data

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning.

Collecting and analyzing soccer-related graffiti with the spatial video technology and GIS: a case study in Krakow, Poland

In this research, the spatial video technology is applied to the collection of soccer-related graffiti locations in the city of Krakow, Poland. Krakow is predestined for this research due to the long and often violent rivalry between fan groups of the two major soccer teams, MKS Cracovia and Wisla Krakow. This form of rivalry is often expressed by the application of graffiti by fans from both clubs, which can be observed in large parts of the city. Graffiti locations were digitized from spatial videos, stored in a Geographic Information System (GIS), and subsequently analyzed to explore (1) the overall spatial pattern, (2) the existence of spatial hotspots, and (3) changes to a previously conducted survey of graffiti locations in 2016. As expected, results indicate that graffiti locations are statistically significantly clustered, with pro-Wisla graffiti mainly concentrating in the north, pro-Cracovia graffiti dominating the south, and pro-Hutnik graffiti mostly found in the east of Krakow. The overall spatial pattern of graffiti locations remained relatively unchanged between the 2016 and 2018 surveys. Besides scientific inquiry, this research provides city officials with important information regarding graffiti locations in Krakow for a broader and more in-depth understanding of their spatial patterns.

Modelling the spatial patterns of ignition causes and fire regime features in southern France: implications for fire prevention policy

A good knowledge of the spatiotemporal patterns of the causes of wildfire ignition is crucial to an effective fire policy. However, little is known about the situation in south-eastern France because the fire database contains unreliable data. We used data for cases with well-established causes from 1973–2013 to determine the location of spatial hotspots, the seasonal distribution, the underlying anthropogenic and environmental drivers and the tendency of five main causes to generate large fires. Anthropogenic ignitions were predominant (88%) near human settlements and infrastructures in the lowlands, whilst lightning-induced fires were more common in the coastal mountains. In densely populated urban areas, small summer fires were predominating, due to the negligence of private individuals around their homes or accidental ignitions near infrastructures. In rural hinterlands, ignitions due to negligence by professionals generate many medium-sized fires from autumn to spring. Intentional and accidental ignitions contribute the most to the total burned area and to large fires. We conclude that socioeconomic factors partially control the fire regime, influencing the timing, spatial distribution and potential size of fires. This improved understanding of why, where and when ignitions occur provides the opportunity for controlling certain causes of ignitions and adapting French policy to global changes.