The R Project for Statistical Computing Classics - John Snow: The London Cholera Epidemic of 1854 It wasn't until 1854 that Cholera struck England once again, that Snow was able to legitimate his argument that Cholera was spread through contaminated food or water. Snow, in investigating the epidemic, began plotting the location of deaths related to Cholera (see illustration). At the time, London was supplied its water by two water companies. One of these companies pulled its water out of the Thames River upstream of the main city while the second pulled its water from the river downstream from the city. A higher concentration of Cholera was found in the region of town supplied by the water company that drew its water form the downstream location. After the panic-stricken officials followed Snow's advice to remove the handle of the Broad Street Pump that supplied the water to this neighborhood, the epidemic was contained. Snow's classic study offers one of the most convincing arguments of the value of understanding and resolving a social problem through the use of spatial analysis.
GRASS GIS - Home PySAL ASU’s GeoDa Center for GeoSpatial Analysis and Computation, a research unit closely affiliated with the School of Geographical Sciences and Urban Planning, develops PySAL, an open source library of computational tools for spatial analysis. PySAL grew out of a collaborative effort spearheaded by Professor Sergio Rey and Luc Anselin, Walter Isard Chair and Director of the School of Geographical Sciences and Urban Planning. The project integrates two analytical tools, STARS and PySpace, that were developed separately by the two researchers prior to their arrival at ASU. PySAL provides a suite of spatial analytical methods that developers can incorporate into their own application development, and that spatial analysts may customize to further their research. For more information, see the latest press release below. PySAL 1.7 press release PySAL 1.5 press release PySAL 1.4 press release PySAL 1.2 press release PySAL 1.1 press release PySAL 1.0 press release User Feedback Download PySAL
More adventures in overlay: point in polygon Counting the number of points in a polygon is a common overlay operation. But unless you’re aware of what happens when points fall on polygon boundaries, or when points fall just outside the coverage of your polygons, you may not be getting the results you expect. Here’s the scenario: I have polygons representing some administrative districts.And I have some points representing households. Each household has an attribute HHSize which is the number of people in the household.I want to count the number of households and the total size of the household in each polygon. The map to the left shows the household points with graduated symbols based on HHSIZE. There are lots of ways to perform the overlay to get the number of households and the sum of household size. Now let’s go back to the Households table and get some statistics. Huh? Finding boundary points To find boundary points, use the Select By Location tool found in the Selection menu of ArcMap. Finding points outside Near Summary Statistics
Lesson 5: Interpolation - From Simple to Advanced | GEOG 586: Geographic Information Analysis Printer-friendly version Introduction In this lesson, we will examine one of the most important methods in all of spatial analysis. Frequently data are only available at a sample of locations when the underlying phenomenon is, in fact, continuous and, at least in principle, measurable at all locations. The problem, then, is to develop reliable methods for 'filling in the blanks.' The most familiar examples of this problem are meterological, where weather station data are available, but we want to map the likely rainfall, snowfall, air temperature, and atmostpheric pressure conditions across the whole study region. The general name for any method designed to 'fill in the blanks' in this way is interpolation. Learning Objectives By the end of this lesson, you should be able to Questions? Please use the 'Week 5 lesson and project discussion' forum to ask for clarification on any of these concepts and ideas.
Live 7.9 Contents — OSGeo-Live 7.9 Documentation OSGeo-Live 10.0 Contents¶ Desktop GIS¶ General GIS viewing, editing, and analysis on the desktop: Browser Facing GIS¶ General GIS viewing, editing and analysis in the browser: OpenLayers3 - [QuickStart] - Browser Mapping LibraryLeaflet - [QuickStart] - Mobile Friendly Interactive MapsCesium - [QuickStart] - 3D globes and 2D maps in a browserGeomajas - [QuickStart] - Browser GIS ClientMapbender - [QuickStart] - Geoportal FrameworkGeoMoose - [QuickStart] - Web GIS PortalCartaro - [QuickStart] - Geospatial CMSGeoNode - [QuickStart] - Geospatial Content Management System Web Services¶ Publishing spatial data to the internet: Data Stores¶ Storing spatial data: PostGIS - [QuickStart] - Spatial DatabaseSpatiaLite - [QuickStart] - Lightweight DatabaseRasdaman - [QuickStart] - Multi-Dimensional Raster DatabasepgRouting - [QuickStart] - Routing for PostGIS Navigation and Maps¶ Spatial Tools¶ Specific analysis tools: Domain Specific GIS¶ Applications targeted at a specific domain: Data¶ Spatial data sets:
SAGA.Matlab Kirill K. Pankratov SaGA is a collection of MATLAB programs dealing with various aspects of geometrical modeling and spatial data analysis. Before proceeding further you are invited to a short tour of the gallery of pictures easily produced with SaGA routines. By the way here is the m-file sagawcm.m which produces the above header picture. This is is the Readme file with brief information describing the SaGA package. Here you can get straight to the SAGA directory where all the programs are stored. To see a list of functions contained in the SaGA Toolbox go to the Contents file. One can transfer the archives containing most of the SaGA toolbox from SAGA_Z directory. The structure and function interdependence of the SaGA toolbox is detailed in the Flowchart. See License file for registration information. The Whatsnew file will contains information about updates and further development of the SaGA Toolbox. And here one can find answers to some Frequently Asked Questions about SaGA.
Statistical Standards and Guidelines | FCSM Skip to page content Statistical Standards and Guidelines The Statistical and Science Policy Branch in the Office of Information and Regulatory Affairs (OIRA) at the Office of Management and Budget (OMB) issues government-wide standards and guidelines to ensure comparability of Federal statistics. OIRA issues guidance on information policy, the Privacy Act, and Federal information collection. Government-wide Standards and Guidelines Agency Standards and Guidelines Other Resources [PDF] indicates a document is in Portable Document Format. Contact Us Send comments, feedback, ...
geoviz - This is a toolkit for geographic visualization and analysis To try the GeoViz Toolkit with Google Flu data pre-loaded, click on the launch button below. Java 6.0+ required. Get Java The GeoViz toolkit is a project derived from the GeoVISTA Studio project. This is alpha quality code, expect things to randomly break, code to be refactored at any time, etc. The most stable current build is available from The API Docs are available from: Previous binary versions are available from Previous source versions are in the repository, of course! YourKit is kindly supporting this open source project with its full-featured Java Profiler. If something has broken that you were enjoying, please let me know at firstname.lastname@example.org.
Welcome to OSGeo-Live 7.9 — OSGeo-Live 7.9 Documentation gstat home page Leveraging Geospatially-Oriented Social Media Communications in Disaster Response Abstract Geospatially-oriented social media communications have emerged as a common information resource to support crisis management. The research presented compares the capabilities of two popular systems used to collect and visualize such information - Project Epic’s Tweak the Tweet (TtT) and Ushahidi. The research uses geospatially-oriented social media gathered by both projects during recent disasters to compare and contrast the frequency, content, and location components of contributed information to both systems. The authors compare how data was gathered and filtered, how spatial information was extracted and mapped, and the mechanisms by which the resulting synthesized information was shared with response and recovery organizations. In addition, the authors categorize the degree to which each platform in each disaster led to actions by first responders and emergency managers. Article Preview Introduction
Project EPIC » Tweak the Tweet Tweak the Tweet Tweak the Tweet is a hashtag-based syntax to help direct Twitter communications for more efficient data extraction for those communicating about disaster events. Use requires modifications of Tweet messages to make information pieces that refer to #location, #status, #needs, #damage and several other elements of emergency communications more machine readable. We have deployed TtT for multiple events during 2010 and 2011, including the Haiti earthquake, the Chile earthquake, the Oil Spill, the Fourmile Canyon file in Boulder, the Queensland floods, Cyclone Yasi, and a variety of other weather emergency events. Resources TtT Client : We have developed a client that promotes tweeting in the correct syntax. Map of Boulder fire geolocated tweets Current map, geolocated tweets Tweak the Tweet by Kate Starbird & Project EPIC is licensed under a Creative Commons Attribution 3.0 Unported License