GIS-aided Evaluation of Evapotranspiration at Multiple Spatial and Temporal Climate Patterns using Geoindicators

Ecological Indicators, Article in Press, 2010

Nazzareno Diodatoa, Michele Ceccarelli, and Gianni Bellocchic

“Multivariate spatial statistics techniques can be efficiently applied to generate fine spatial patterns of climate data in presence of an appropriate multivariate spatial structure over ungauged mountainous basins. However, they can become unsuitable when the data available over complex regions are sparse and affected by discordant spatial scales in primary and (colocated)-auxiliary variables. This is the case of actual evapotranspiration (AET). Combining GIS and geoindicators (e.g., topographical and vegetational indices), we proposed an upscaling procedure to overcome this problem, transforming a preliminary-smoothed macro-scale pattern (AET grid-data), into a local-scale pattern.

“The procedure was applied to a cropland test site at Mediterranean sub-regional basin scale (Tammaro, South Italy) to develop a climatological baseline estimation of AET refined at slope scale. After the upscaling, the most frequent estimated AET values were about 550 mm yr−1 (with quasi-normal distribution), while underestimations were observed in the preliminary, smoothed map (positively skewed distribution with mean 460 mm yr−1). The upscaling allowed the influence of the topographic factor to emerge, with a wider range of values (about 300–900 mm yr−1) being estimated and substantially not visible in the smoothed pattern. A temporal climate pattern of soil water depletion in the growing season was also shown as reflected in the increase of AET flux in the period 1991–2008 in comparison to the precedent climate (1961–1990).”

Spatial Analysis of Hospitalization for Heart Diseases in Vale do Paraíba

Arquivos Brasileiros de Cardiologia, 2010 Apr 30

Soares PA and Nascimento LF

“Ischemic heart diseases (IHD) are important causes of death in the Vale do Paraíba paulista. To identify patterns of spatial distribution of hospitalizations for acute myocardial infarction (AMI) and IHD in the Vale do Paraíba paulista. This was an ecological study using exploratory spatial analysis of hospitalization data for acute myocardial infarction and ischemic heart disease in the Vale do Paraíba between 2004 and 2005. The statistical analysis used spatial georeferenced databases of 35 municipalities and spatial statistics routines. The admission data were obtained from the Portal Datasus of the Ministry of Health. The variables were the number of admissions for males and females aged over 30 years. To evaluate the spatial dependence we used the autocorrelation coefficients of Global Moran and Local Moran’s index. We also analyzed the correlations between variables, using the TerraView program. The level of significance was 5%. Among 6,287 admissions, the rates were 161.66/100 thousand. Of the total of 35 municipalities, 31.4% had rates above average. The coefficient of Moran (global) showed a statistical significance. Local indexes showed clusters, indicating a cluster of 9 municipalities in which there was spatial dependence with their own dynamics. In the mid Vale do Paraíba paulista, the spatial analysis identified spatial clusters of hospitalizations due to acute myocardial infarction and ischemic heart disease, allowing intervention to reduce rates.”

Jordan Romero Uses GIS to Share Everest Experience

Mapping Web Site Shows Location, Weather, Photos, and Tweets

Thirteen-year-old Jordan Romero partnered with ESRI to share his Mount Everest climb with the world via geographic information system (GIS) technology. The Jordan Romero Web site features an ESRI GIS mapping application that integrates Web services to track Romero’s journey. The application lets the public see Team Romero’s location in near real time, explore daily tracks, view distance and elevation statistics, and browse weather and route information. The application also gives geographic context to social media—for example, Flickr photos and Twitter posts from the team throughout the trip.

ESRI is mapping the expedition with a lightweight, user-friendly Web application that uses the ArcGIS API for Microsoft Silverlight/WPF and data from ArcGIS Online. The API is used to deliver live information from other Web services. This includes the latest GPS messages from SPOT (updated as frequently as every 10 minutes), current elevation and distances from ArcGIS Server, daily weather forecasts from meteoexploration, and social media streams. ArcGIS Online provides the map layers and imagery of the 29,035-foot mountain and surrounding area.

“I know technology is saving lives every day, and in this case, it does make our team safer and in contact with rescue and even friends and family,” says Romero. “I also appreciate that now my generation is going to experience better technology. To think my peers are watching my every step; I am so grateful. And perhaps through this technology, I’m actually going to inspire some kids to get out and set some big goals and dreams.”

Team Romero and ESRI have created a single and comprehensive solution to track, map, and show up-to-date information about the climb, so the rest of the world can see Romero’s progress at the top of the world. Educators are using the tool in the classroom, leveraging the application to make it easy and fun to learn about the feat and experience it right along with Romero.

Romero has already climbed six of the “Seven Summits,” the tallest mountain on each of the seven continents. He is striving to become the youngest person ever to summit Everest, beating the current record by three years. Team Romero left for Everest on April 5 and is planning to summit sometime this month. Romero is joined by his father, Paul Romero, and stepmom Karen Lundgren. The three have achieved all six summits together. There are several interpretations of the “Seven Summits,” so Romero is also intending to tackle Antarctica’s Vinson Massif in December 2010.

More information about Romero is available at www.jordanromero.com. Click Live on Everest to see where the climbers are now.

[Source: ESRI press release]

Spatial and Temporal Analysis of a Phytophthora Megakarya Epidemic in a Plantation in the Centre of Cameroon

2009. In : Towards rational cocoa production and efficient use for a sustainable word coca economy : 16th International Cocoa Resarch Conference, 2009-11-16/2009-11-21, Bali, Indonésie.

Ten Hoopen G.M., Sounigo O., Babin R., Yédé M., Dikwe G., and Cilas C..

“Black pod rot of cocoa caused by Phytophthora megakarya causes significant losses in Cameroon. Studying the spatial and temporal disease dynamics of P. megakarya provides useful information on the mechanisms of dispersal and the physiological and biological factors that are important for its spread and ultimately its management. Therefore, we studied the spatial and temporal development of a P. megakarya epidemic in a plantation in the Centre region of Cameroon over two production seasons. A map was made of a cocoa plantation of 2.61 ha, containing a total of 2536 cocoa trees and 438 neighbor/shade trees. Each cocoa and neighbor tree was assigned x and y coordinates by projecting the plantation map onto an orthonormal grid. From a total of 421 cocoa trees located throughout the plantation, production and pod rot data were collected weekly. Subsequently, for both production seasons, the spatial relation between the numbers of rotten pods harvested from the observed cocoa trees was analyzed by semivariogram analysis. Block kriging was used as an interpolation method. Analysis of the semivariograms revealed a spatial dependence of pod rot distribution. In all cases where the theoretical model fitted well to the actual data the semivariogram was exponential. The estimated dispersion range of P. megakarya varied from 2.7 to 6.9 m in 2006 (mean 5.1, SD 0.56 m) and 3.3 to 6.9 m in 2007 (mean 5.6, SD 0.94 m). Kriging maps revealed the simultaneous appearance of multiple infection points throughout the plantation at distances larger than the dispersion range. Infection hot spots were located at similar locations for both years. Based on these results, we hypothesize that primary inoculum is the main determinant for the spatial and temporal development of an epidemic at the plantation level and that secondary inoculum is mainly responsible for the within-tree temporal development of an epidemic. Therefore, more attention should be given to reducing primary inoculum levels of P. megakarya in order to improve control efficacy.”

Geostatistical Simulation for the Assessment of Regional Soil Pollution

Geographical Analysis, Volume 42 Issue 2 (April 2010) p 121-135

Marc Van Meirvenne and Tariku Meklit

“Regional scale inventories of heavy metal concentrations in soil increasingly are being done to evaluate their global patterns of variation. Sometimes these global pattern evaluations reveal information that is not identified by more detailed studies. Geostatistical methods, such as stochastic simulation, have not yet been used routinely for this purpose in spite of their potential. To investigate such a use of geostatistical methods, we analyzed a data set of 14,674 copper and 12,441 cadmium observations in the topsoil of Flanders, Belgium, covering 13,522 km2. Outliers were identified and removed, and the distributions were spatially declustered. Copper was analyzed using sequential Gaussian simulation, whereas for cadmium we used sequential indicator simulation because of the large proportion (43%) of censored data. We complemented maps of the estimated values with maps of the probability of exceeding a critical sanitation threshold for agricultural land use. These sets of maps allowed the identification of regional patterns of increased metal concentrations and provided insight into their potential causes. Mostly areas with known industrial activities (such as lead and zinc smelters) could be delineated, but the effects of shells fired during the First World War were also identified.”