Partnership Enhances Ushahidi Web Platform with Extensive GIS Data, Advanced Analytic Tools
Esri today announced a partnership with Ushahidi that will improve the collection and use of crowd-sourced information during large-scale emergencies. Ushahidi is a nonprofit organization that allows local observers to submit reports to the Ushahidi Web platform using their mobile phones or the Internet during a crisis. Esri is providing software, services, and training to support Ushahidi’s Web platform. The result is better information more readily accessible by decision makers and the public.
Both victims and witnesses of a crisis or disaster can request assistance or report conditions using text messaging capabilities from their personal phone or Internet-enabled device. These reports are collected, geo-referenced and then mapped on Ushahidi’s web-based map. Esri and Ushahidi will now work together to make this information available to more people, including those using Esri geographic information system (GIS) technology. This will provide access to Ushahidi information to relief and response organizations that use GIS analysis and modeling for humanitarian response and relief.
“Ushahidi has provided an invaluable information service during crisis events, such as the Haiti earthquake, by supplying a social media platform to capture and communicate critical information for response and relief services,” says Russ Johnson, director of public safety solutions, Esri. “Esri wants to support these efforts by making available GIS tools that assist in analyzing, displaying, and publishing critical information on the Ushahidi platform.”
“Our strategic relationship with Esri represents an important step forward for our combined user base,” says Patrick Meier, director of crisis mapping at Ushahidi. “Esri’s technology will provide Ushahidi users with access to extensive GIS data and advanced analytical tools. Esri users will also have the ability to contribute to Ushahidi mapping efforts in more seamless ways and use this data for further analysis.”
Ushahidi, which is Swahili for “testimony” or “witness,” first established itself by developing a Web site created in the aftermath of Kenya’s disputed 2007 presidential election. The site collected eyewitness reports of violence sent in by e-mail and text messages and placed them on maps. The free and open-source software platform developed for the site has since been improved and used for a number of events. For instance, in the aftermath of the Haiti earthquake, thousands of people sent text messages for help. That information was mapped and used by emergency responders to provide needed resources quickly where they were needed most.
[Source: Esri press release]
Sustainability Science, Volume 4, Number 2, 2009, 301-313
Kan-ichiro Matsumura, Rover J. Hijmans, Yann Chemin, Christopher D. Elvidge, Kenji Sugimoto, Wenbin Wu, Yang-won Lee and Ryosuke Shibasaki
“Rice plays a major role in the global supply and demand for sustainable food production. The constraints of maintaining sustainable rice production are closely linked to the relationship between the distribution patterns of human activity on the planet and economic growth. Global patterns of rice production can be mapped by using various criteria linked to domestic income, population patterns, and associated satellite brightness data of rice-producing regions. Prosperous regions have more electric lighting, and there are documented correlations between gross domestic product (GDP) and nighttime light. We chose to examine global rice production patterns on a geographical basis. For the purposes of this study, each country is considered to be made up of regions, and rice production is discussed in terms of regional distribution. A region is delineated by its administrative boundaries; the number of regions where rice is produced is about 13,839. We used gridded spatial population distribution data overlain by nocturnal light imagery derived from satellite imagery. The resultant relationship revealed a correlation between regional income (nominal values of GDP were used) and rice production in the world. The following criteria were used to examine the supply and demand structure of rice. Global rice consumption = “caloric rice consumption per capita per day” multiplied by “regional population values”. Regional rice yields = “country-based production” divided by “harvested area” (multiple harvests are taken into account). Regional rice production = “regional harvested areas” multiplied by “rice yield values”. We compared regional rice consumption and production values according to these methods. Analysis of the data sets generated a map of rice supply and demand. Inter-regional shipping costs were not accounted for. This map can contribute to the understanding of food security issues in rice-producing regions and to estimating potential population values in such regions.”
Environmental Monitoring and Assessment, Vol. 170, No. 1. (1 November 2010), pp. 407-416-416
Yong-Hui Yang, Feng Zhou, Huai-Cheng Guo, Hu Sheng, Hui Liu, Xu Dao, Cheng-Jie He
“Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003â2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12Â months into two groups, AugustâSeptember and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, CODMn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NHâN, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, CODCr, CODMn, NHâN, TP, and EC. In addition, boxâwhisker plots and GIS further facilitated and supported the multivariate analysis results.”