Top GIS Programming Languages You Should Use
No surprises and to help you avoid the one language per page plus ads presentation:
Best guide is to use whatever other people you work with use, so you can share experience and techniques. All of these languages have more documentation, examples, etc., than any one person can master. Share that load and you will all be more productive.
The Maps API offers:
Recent Imagery
A curated satellite imagery layer of the entire globe. More than 80% of the Earth’s landmass is covered with high-resolution (30 cm-60 cm) imagery, supplemented with cloud-free LandSat 8 as a backdrop.
Street Map
An accurate, seamless street reference map. Based on contributions from the OpenStreetMap community, this layer combines global coverage with essential “locals only” perspectives.
Terrain Map
A seamless, visually appealing terrain perspective of the planet. Shaded terrain with contours guide you through the landscape, and OpenStreetMap reference vectors provide complete locational context.
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Prices start at $5/month and go up. (5,000 map views for $5.)
BTW, 30 cm is 11.811 inches, just a little less than a foot.
For planning constructive or disruptive activities, that should be sufficient precision.
I haven’t tried the service personally but the resolution of the imagery compels me to mention it.
Enjoy!
How Is GIS Being Used To Map Resistance And Political Protests? by Sarah Bond.
From the post:
In the days since Donald Trump became president on January 20, 2017, millions of protestors have gathered in cities both big and small across the globe. And while presidential counselor Kellyanne Conway told Chuck Todd on NBC’s “Meet The Press” that, “There’s really no way to quantify crowd numbers“–digital humanists, data scientists, librarians and geographers beg to differ.
Let’s check in on some projects attempting to use GIS to visualize the recent political protests, preserve data and keep activists informed.
Despite Conway’s remarks, a Google Doc started by Jeremy Pressman at the University of Connecticut and Erica Chenoweth of the University of Denver soon began to collect crowd-sourced estimates from the Women’s Marches on January 20, 2017 organized by city, state and country. As they say on the public spreadsheet, “We are not collecting this data as part of a research project. We are doing this in the public interest. We are not affiliated with any other efforts to collect data on the demonstrations.” Over at Vox, graphics reporter Sarah Frostenson turned their data into a static map. Other researchers also weighed in. Doug Duffy, a PhD candidate at the University of Toronto, made an interactive map of Pressman and Chenoweth’s data here and posted the visualization to his GitHub page. He even cleaned the data for easy download and reuse (with attribution) by others.
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The post has links to a number of other projects that are mapping data related to resistance and political protests.
If that wasn’t encouraging enough, Sarah’s post appeared in Forbes, which isn’t known for being a hotbed of criminal syndicalism.
😉
Can using GIS to plan resistance and political protests be very far away?
GRASS GIS is very relevant for anyone wanting to use data science to plan protests.
You can plan a protest using corner store maps, but those are unlikely to have alleys, bus stops, elevation, litter cans, utilities, and other details.
Other participants will have all that data and more so evening up the odds is a good idea.
Apologizes for the long quote but I don’t know which features/capabilities of GRASS GIS will be most immediately relevant for you.
From the general overview page:
General Information
Geographic Resources Analysis Support System, commonly referred to as GRASS GIS, is a Geographic Information System (GIS) used for data management, image processing, graphics production, spatial modelling, and visualization of many types of data. It is Free (Libre) Software/Open Source released under GNU General Public License (GPL) >= V2. GRASS GIS is an official project of the Open Source Geospatial Foundation.
Originally developed by the U.S. Army Construction Engineering Research Laboratories (USA-CERL, 1982-1995, see history of GRASS 1.0-4.2 and 5beta), a branch of the US Army Corp of Engineers, as a tool for land management and environmental planning by the military, GRASS GIS has evolved into a powerful utility with a wide range of applications in many different areas of applications and scientific research. GRASS is currently used in academic and commercial settings around the world, as well as many governmental agencies including NASA, NOAA, USDA, DLR, CSIRO, the National Park Service, the U.S. Census Bureau, USGS, and many environmental consulting companies.
The GRASS Development Team has grown into a multi-national team consisting of developers at numerous locations.
In September 2006, the GRASS Project Steering Commitee was formed which is responsible for the overall management of the project. The PSC is especially responsible for granting SVN write access.
General GRASS GIS Features
GRASS GIS contains over 350 modules to render maps and images on monitor and paper; manipulate raster, and vector data including vector networks; process multispectral image data; and create, manage, and store spatial data. GRASS GIS offers both an intuitive graphical user interface as well as command line syntax for ease of operations. GRASS GIS can interface with printers, plotters, digitizers, and databases to develop new data as well as manage existing data.
GRASS GIS and support for teams
GRASS GIS supports workgroups through its LOCATION/MAPSET concept which can be set up to share data and the GRASS installation itself over NFS (Network File System) or CIFS. Keeping LOCATIONs with their underlying MAPSETs on a central server, a team can simultaneously work in the same project database.
GRASS GIS capabilities
- Raster analysis: Automatic rasterline and area to vector conversion, Buffering of line structures, Cell and profile dataquery, Colortable modifications, Conversion to vector and point data format, Correlation / covariance analysis, Expert system analysis , Map algebra (map calculator), Interpolation for missing values, Neighbourhood matrix analysis, Raster overlay with or without weight, Reclassification of cell labels, Resampling (resolution), Rescaling of cell values, Statistical cell analysis, Surface generation from vector lines
- 3D-Raster (voxel) analysis: 3D data import and export, 3D masks, 3D map algebra, 3D interpolation (IDW, Regularised Splines with Tension), 3D Visualization (isosurfaces), Interface to Paraview and POVray visualization tools
- Vector analysis: Contour generation from raster surfaces (IDW, Splines algorithm), Conversion to raster and point data format, Digitizing (scanned raster image) with mouse, Reclassification of vector labels, Superpositioning of vector layers
- Point data analysis: Delaunay triangulation, Surface interpolation from spot heights, Thiessen polygons, Topographic analysis (curvature, slope, aspect), LiDAR
- Image processing: Support for aerial and UAV images, satellite data (optical, radar, thermal), Canonical component analysis (CCA), Color composite generation, Edge detection, Frequency filtering (Fourier, convolution matrices), Fourier and inverse fourier transformation, Histogram stretching, IHS transformation to RGB, Image rectification (affine and polynomial transformations on raster and vector targets), Ortho photo rectification, Principal component analysis (PCA), Radiometric corrections (Fourier), Resampling, Resolution enhancement (with RGB/IHS), RGB to IHS transformation, Texture oriented classification (sequential maximum a posteriori classification), Shape detection, Supervised classification (training areas, maximum likelihood classification), Unsupervised classification (minimum distance clustering, maximum likelihood classification)
- DTM-Analysis: Contour generation, Cost / path analysis, Slope / aspect analysis, Surface generation from spot heigths or contours
- Geocoding: Geocoding of raster and vector maps including (LiDAR) point clouds
- Visualization: 3D surfaces with 3D query (NVIZ), Color assignments, Histogram presentation, Map overlay, Point data maps, Raster maps, Vector maps, Zoom / unzoom -function
- Map creation: Image maps, Postscript maps, HTML maps
- SQL-support: Database interfaces (DBF, SQLite, PostgreSQL, mySQL, ODBC)
- Geostatistics: Interface to “R” (a statistical analysis environment), Matlab, …
- Temporal framework: support for time series analysis to manage, process and analyse (big) spatio-temporal environmental data. It supports querying, map calculation, aggregation, statistics and gap filling for raster, vector and raster3D data. A temporal topology builder is available to build spatio-temporal topology connections between map objects for 1D, 3D and 4D extents.
- Furthermore: Erosion modelling, Landscape structure analysis, Solution transport, Watershed analysis.
See also the Applications page in the Wiki and the Wikipedia entry.
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Spatial Module in OrientDB 2.2
From the post:
In versions prior to 2.2, OrientDB had minimal support for storing and retrieving GeoSpatial data. The support was limited to a pair of coordinates (latitude, longitude) stored as double in an OrientDB class, with the possibility to create a spatial index against those 2 coordinates in order to speed up a geo spatial query. So the support was limited to Point.
In OrientDB v.2.2 we created a brand new Spatial Module with support for different types of Geometry objects stored as embedded objects in a user defined class
- Point (OPoint)
- Line (OLine)
- Polygon (OPolygon)
- MultiPoint (OMultiPoint)
- MultiLine (OMultiline)
- MultiPolygon (OMultiPlygon)
- Geometry Collections
Along with those data types, the module extends OrientDB SQL with a subset of SQL-MM functions in order to support spatial data.The module only supports EPSG:4326 as Spatial Reference System. This blog post is an introduction to the OrientDB spatial Module, with some examples of its new capabilities. You can find the installation guide here.
Let’s start by loading some data into OrientDB. The dataset is about points of interest in Italy taken from here. Since the format is ShapeFile we used QGis to export the dataset in CSV format (geometry format in WKT) and import the CSV into OrientDB with the ETL in the class Points and the type geometry field is OPoint.
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The enhanced spatial functions for OrientDB 2.2 reminded me of this passage in “Silences and Secrecy: The Hidden Agenda of Cartography in Early Modern Europe:”
Some of the most clear-cut cases of an increasing state concern with control and restriction of map knowledge are associated with military or strategic considerations. In Europe in the sixteenth and seventeenth centuries hardly a year passed without some war being fought. Maps were an object of military intelligence; statesmen and princes collected maps to plan, or, later, to commemorate battles; military textbooks advocated the use of maps. Strategic reasons for keeping map knowledge a secret included the need for confidentiality about the offensive and defensive operations of state armies, the wish to disguise the thrust of external colonization, and the need to stifle opposition within domestic populations when developing administrative and judicial systems as well as the more obvious need to conceal detailed knowledge about fortifications. (reprinted in: The New Nature of Maps: Essays in the History of Cartography, by J.B. Harley: Paul Laxton, John Hopkins, 2001. page 89)
I say “reminded me,” better to say increased my puzzling over the widespread access to geographic data that once upon a time had military value.
Is it the case that “ordinary maps,” maps of streets, restaurants, hotels, etc., aren’t normally imbued (merged?) with enough other information to make them “dangerous?”
If that’s true, the lack of commonly available “dangerous maps” is a disadvantage to emergency and security planners.
You can’t plan for the unknown.
Or to paraphrase Dibert: “Ignorance is not a reliable planning guide.”
How would you cure the ignorance of “ordinary” maps?
PS: While hunting for the quote, I ran across The Power of Maps by Denis Wood; with John Fels. Which has been up-dated: Rethinking the power of maps by Denis Wood; with John Fels and John Krygier. I am now re-reading the first edition and awaiting for the updated version to arrive.
Neither book is a guide to making “dangerous” maps but may awaken in you a sense of the power of maps and map making.
D3 Maps without the Dirty Work by
From the post:
For those like me who aren’t approaching mapping in D3 with a GIS background in tow, you may find the propretary goe data structures hard to handle. Thankfully, Scott Murray lays out a simple process in his most recent course through JournalismCourses.org. By the time you are through reading this post you’ll have the guide post needed from mapping any of the data sets found on Natural Earths website in D3.
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First in a series of posts on D3 rendering for maps. Layers of D3 renderings is coming up next.
Enjoy!
New Spatial Aggregation Tutorial for GIS Tools for Hadoop by Sarah Ambrose.
Sarah motivates you to learn about spatial aggregation, aka spatial binning, with two visualizations of New York taxi data:
No aggregation:
Aggregation:
Now that I have your attention, ;-), from the post:
This spatial analysis ability is available using the GIS Tools for Hadoop. There is a new tutorial posted that takes you through the steps of aggregating taxi data. You can find the tutorial here.
Enjoy!
3 Reasons Every Business Should Think About Location Intelligence
From the post:
The ease-of-use of mobile apps like Google Maps and Strava (which is now being used for urban planning) has inspired a lot of companies to start thinking differently about location. Consequently, a lot of IT professionals are getting asked to create location-based applications.
“IT is now being required to build spatially-aware or enabled applications, without a history of working with these technologies,” says Clarence Hempfield, Director of Product Management at Pitney Bowes. “That means organizations like Pitney Bowes have had to deliver these capabilities in such a way that a non-GIS expert can build and deliver spatial applications, without that foundation of years of working with the technology.”
This rapid consumerization of GIS technology has allowed anyone with a smartphone to use aspects of GIS technology with a few taps of their fingers, revealing valuable location intelligence data that they use to find new stores, directions and more… and consumers are expecting companies to follow suit.
…
I was struck by the contrast between the claim “…and consumers are expecting companies to follow suit,” and the three reasons given for location intelligence:
- Local Can Amplify Social.
- Data and Maps Can Help You Plan for the Future.
- Location Can Super-Charge BI
None of those reasons confer benefits upon consumers. Demographics are used to project a consumer’s expected choices, limiting your range of selection to the expectations of the business. What if you or I are outliers? Does that simply not count?
Consumer research on consumers wanting companies to track them and combine data with other data sources? It may well exist and if it does, please put a pointer in the comments.
If you are not one of those consumers who wants to be tracked, invest in a cellphone pouch that blocks tracking of your phone.
Spatial Data on the Web Working Group
From the webpage:
The mission of the Spatial Data on the Web Working Group is to clarify and formalize the relevant standards landscape. In particular:
- to determine how spatial information can best be integrated with other data on the Web;
- to determine how machines and people can discover that different facts in different datasets relate to the same place, especially when ‘place’ is expressed in different ways and at different levels of granularity;
- to identify and assess existing methods and tools and then create a set of best practices for their use;
where desirable, to complete the standardization of informal technologies already in widespread use.
The Spatial Data on the Web WG is part of the Data Activity and is explicitly chartered to work in collaboration with the Open Geospatial Consortium (OGC), in particular, the Spatial Data on the Web Task Force of the Geosemantics Domain Working Group. Formally, each standards body has established its own group with its own charter and operates under the respective organization’s rules of membership, however, the ‘two groups’ will work together very closely and create a set of common outputs that are expected to be adopted as standards by both W3C and OGC and to be jointly branded.
Read the charter and join the Working Group.
Just when I think the W3C has broken free of RDF/OWL, I see one of the deliverables is “OWL Time Ontology.”
Some people never give up.
There is a bright shiny lesson about the success of deep learning. It doesn’t start with any rules. Just like people don’t start with any rules.
Logic isn’t how we get anywhere. Logic is how we justify our previous arrival.
Do you see the difference?
I first saw this in a tweet by Marin Dimitrov.
Turf: GIS for web maps by Morgan Herlocker.
From the post:
Turf is GIS for web maps. It’s a fast, compact, and open-source JavaScript library that implements the most common geospatial operations: buffering, contouring, triangular irregular networks (TINs), and more. Turf speaks GeoJSON natively, easily connects to Leaflet, and is now available as a Mapbox.js plugin on our cloud platform. We’re also working to integrate Turf into our offline products and next-generation map rendering tools.
(Population data from the US Census transformed in read-time into population isolines with turf-isoline.)
The image in the original post is interactive. Plus there are several other remarkable examples.
Turf is part of a new geospatial infrastructure. Unlike the ArcGIS API for JavaScript, Turf can run completely client-side for all operations, so web apps can work offline and sensitive information can be kept local. We’re constantly refining Turf’s performance. Recent research algorithms can make operations like clipping and buffering faster than ever, and as JavaScript engines like V8 continue to optimize, Turf will compete with native code.
Can you imagine how “Steal This Book” would have been different if Abbie Hoffman had access to technology such as this?
Would you like to try? 😉
Digital Mapping + Geospatial Humanities by Fred Gibbs.
From the course description:
We are in the midst of a major paradigm shift in human consciousness and society caused by our ubiquitous connectedness via the internet and smartphones. These globalizing forces have telescoped space and time to an unprecedented degree, while paradoxically heightening the importance of local places.
The course explores the technologies, tools, and workflows that can help collect, connect, and present online interpretations of the spaces around us. Throughout the week, we’ll discuss the theoretical and practical challenges of deep mapping (producing rich, interactive maps with multiple layers of information). Woven into our discussions will be numerous technical tutorials that will allow us to tell map-based stories about Albuquerque’s fascinating past.
This course combines cartography, geography, GIS, history, sociology, ethnography, computer science, and graphic design. While we cover some of the basics of each of these, the course eschews developing deep expertise in any of these in favor of exploring their intersections with each other, and formulating critical questions that span these normally disconnected disciplines. By the end, you should be able to think more critically about maps, place, and our online experiences with them.
We’ll move from creating simple maps with Google Maps/Earth to creating your own custom, interactive online maps with various open source tools like QGIS, Open Street Map, and D3 that leverage the power of open data from local and national repositories to provide new perspectives on the built environment. We’ll also use various mobile apps for data collection, online exhibit software, (physical and digital) historical archives at the Center for Southwest Research. Along the way we’ll cover the various data formats (KML, XML, GeoJSON, TopoJSON) used by different tools and how to move between them, allowing you to craft the most efficient workflow for your mapping purposes.
Course readings that aren’t freely availabe online (and even some that are) can be accessed via the course Zotero Library. You’ll need to be invited to join the group since we use it to distribute course readings. If you are not familiar with Zotero, here are some instructions.
All of that in a week! This week as a matter of fact.
One of the things I miss about academia are the occasions when you can concentrate on one subject to the exclusion of all else. Of course, being unmarried at that age, unemployed, etc. may have contributed to the ability to focus. 😉
Just sampled some of the readings and this appears to be a really rocking course!
Esri Allows Federal Agencies To Open GIS Mapping Data To The Public by Alexander Howard.
From the post:
A debate in the technology world that’s been simmering for years, about whether mapping vendor Esri will allow public geographic information systems (GIS) to access government customers’ data, finally has an answer: The mapping software giant will take an unprecedented step, enabling thousands of government customers around the U.S. to make their data on the ArcGIS platform open to the public with a click of a mouse.
“Everyone starting to deploy ArcGIS can now deploy an open data site,” Andrew Turner, chief technology officer of Esri’s Research and Development Center in D.C., said in an interview. “We’re in a unique position here. Users can just turn it on the day it becomes public.”
Government agencies can use the new feature to turn geospatial information systems data in Esri’s format into migratable, discoverable, and accessible open formats, including CSVs, KML and GeoJSON. Esri will demonstrate the ArcGIS feature in ArcGIS at the Federal Users Conference in Washington, D.C. According to Turner, the new feature will go live in March 2014.
I’m not convinced that GIS data alone is going to make government more transparent but it is a giant step in the right direction.
To have even partial transparency in government, not only would you need GIS data but to have that correlated with property sales and purchases going back decades, along with tracing the legal ownership of property past shell corporations and holding companies, to say nothing of the social, political and professional relationships of those who benefited from various decisions. For a start.
Still, the public may be a better starting place to demand transparency with this type of data.
Free GIS Data by Robin Wilson.
Over 300 GIS data sets. As of 7 December 2013, last updated 6 December 2013.
A very wide ranging collection of “free” GIS data.
Robin recommends you check the licenses of individual data sets. The meaning of “free” varies from person to person.
If you discover “free” GIS resources not listed on Robin’s page, drop him a note.
I first saw this in Pete Warden’s Five Short Links for November 30, 2013.
From the webpage:
JQVMap is a jQuery plugin that renders Vector Maps. It uses resizable Scalable Vector Graphics (SVG) for modern browsers like Firefox, Safari, Chrome, Opera and Internet Explorer 9. Legacy support for older versions of Internet Explorer 6-8 is provided via VML.
Whatever your source of data, cellphone location data, user observation, etc., rendering it to a geographic display may be useful.
Lucene 4.X is a great tool for analyzing cellphone location data (Did you really think only the NSA has it?).
Chilamakuru Vishnu gets us started with a code heavy post with the promise of:
My Next Blog Post will talk about how to implement advanced spatial queries like
geoInterseting – where one polygon intersects with another polygon/line.
geoWithIn – where one polygon lies completely within another polygon.
Or you could obtain geolocation data by other means.
I first saw this at DZone.
Spatially Visualize and Analyze Vast Data Stores with Esri’s GIS Tools for Hadoop
From the post:
Perhaps the greatest untapped IT resource available today is the ability to spatially analyze and visualize Big Data. As part of its continuing effort to expand the use of geographic information system (GIS) technology among web, mobile, and other developers, Esri has launched GIS Tools for Hadoop. The toolkit removes the obstacles of building map applications for developers to truly capitalize on geoenabling Big Data within Hadoop—the popular open source data management framework. Developers now will be able to answer the where questions in their large data stores.
“Hadoop’s method of processing volumes of information directly addresses the most significant challenge facing IT today,” says Marwa Mabrouk, product manager at Esri. “Enabling Hadoop with spatial capabilities is part of Esri’s continued effort to derive more value from Big Data through spatial analysis.”
Processing and displaying Big Data on maps requires functionality that core Hadoop lacks. GIS Tools for Hadoop extends the Hadoop platform with a series of libraries and utilities that connect Esri ArcGIS to the Hadoop environment. It allows ArcGIS users to export map data in HDFS format—Hadoop’s native file system—and intersect it with billions of records stored in Hadoop. Results can be either directly saved to the Hadoop database or reimported back to ArcGIS for higher-level geoprocessing and visualization.
GIS Tools for Hadoop includes the following:
- Sample tools and templates that demonstrate the power of GIS
- Spatial querying inside Hadoop using Hive—Hadoop’s ad hoc querying module
- Geometry Library to build spatial applications in Hadoop
“GIS Tools for Hadoop not only introduces spatial analysis to Hadoop but creates a looping workflow that pulls Big Data into the ArcGIS environment,” says Mansour Raad, senior software architect at Esri. “It provides tools for Hadoop users who need to visualize Big Data on maps.”
Esri recognizes Big Data as a challenge that community-level involvement can help solve. As such, Esri provides GIS Tools for Hadoop as an open source product available on GitHub. Esri encourages users to download the toolkit, report issues, and actively contribute to improving the tools through the GitHub system.
To download GIS Tools for Hadoop, visit http://esri.github.com/gis-tools-for-hadoop.
Once you have where, your topic map can merge in who, what, why and how.
Building Attribute and Value Crosswalks Using Esri’s Data Interoperability Extension by Nathan Lebel.
From the post:
The Esri Data Interoperability Extension gives GIS professionals the ability to build complex spatial extraction, transformation, and loading (ETL) tools. Traditionally the crosswalking of feature classes and attributes is done prior to setting up the migration tools and is used only as a guide. The drawback to this method is that it takes a considerable amount of time to build the crosswalks and then to build the ETL tools.
GISI’s article, “Building Attribute and Value Crosswalks in ESRI Data Interoperability Extension the Scalable/Dynamic Way” outlines the use of the SchemaMapper transformer within Data Interoperability Extension which can pull crosswalk information directly from properly formatted tables. For large projects this means you can store crosswalk information in a single repository and point each ETL tool to that repository without needing to manage multiple crosswalk documents. For projects that might change during the lifecycle of the project the use of SchemaMapper means that changes can be made to the repository without requiring any additional changes to the ETL tool. There are three examples used in this article which encompasses a majority of crosswalking tasks; feature class to feature class, attribute to attribute, and attribute value to attribute value crosswalking. All of the examples use CSV files to store the crosswalk information; however the transformer can pull directly from RDBMS tables as well which gives you the ability to build a user interface to create and update crosswalks which is recommended for large scale projects.
The full article can be accessed on GISI’s blog or as a PDF or Ebook in either EPUB or Kindle or format.
If you have time, please read the original article. Obtain it from the links listed in the final paragraph.
I need for you to verify my reading of the process described in that article.
As far as I can tell, the author never say “why” or on what basis the various mappings are being made.
I would be hard pressed to duplicate the mapping based on the information given about the original data sources.
Having an opaque mapping can be useful, as the article says but what if I stumble upon the mapping five years from now? Or two years? Or perhaps even six months from now?
Specifying the “why” of a mapping is something topic maps are uniquely qualified to do.
You can define merging rules that require the basis for mapping to be specified.
If that basis is absent, no merging occurs.
I encountered the gvSIG site while tracking down the latest release of i3Geo.
From its mission statement:
The gvSIG project was born in 2004 within a project that consisted in a full migration of the information technology systems of the Regional Ministry of Infrastructure and Transport of Valencia (Spain), henceforth CIT, to free software. Initially, It was born with some objectives according to CIT needs. These objectives were expanded rapidly because of two reasons principally: on the one hand, the nature of free software, which greatly enables the expansion of technology, knowledge, and lays down the bases on which to establish a community, and, on the other hand, a project vision embodied in some guidelines and a plan appropriate to implement it.
Some of the software projects you will find at gvSIG are:
gvSIG Desktop
gvSIG is a Geographic Information System (GIS), that is, a desktop application designed for capturing, storing, handling, analyzing and deploying any kind of referenced geographic information in order to solve complex management and planning problems. gvSIG is known for having a user-friendly interface, being able to access the most common formats, both vector and raster ones. It features a wide range of tools for working with geographic-like information (query tools, layout creation, geoprocessing, networks, etc.), which turns gvSIG into the ideal tool for users working in the land realm.
gvSIG Mobile
gvSIG Mobile is a Geographic Information System (GIS) aimed at mobile devices, ideal for projects that capture and update data in the field. It’s known for having a user-friendly interface, being able to access the most common formats and a wide range of GIS and GPS tools which are ideal for working with geographic information.
gvSIG Mobile aims at broadening gvSIG Desktop execution platforms to a range of mobile devices, in order to give an answer to the needings of a growing number of mobile solutions users, who wish to use a GIS on different types of devices.
So far, gvSIG Mobile is a Geographic Information System, as well as a Spatial Data Infrastructures client for mobile devices. Such a client is also the first one licensed under open source.
I3Geo
i3Geo is an application for the development of interactive web maps. It integrates several open source applications into a single development platform, mainly Mapserver and OpenLayers. Developed in PHP and Javascript, it has functionalities that allows the user to have better control over the map output, allowing to modify the legend of layers, to apply filters, to perform analysis, etc.
i3Geo is completely customizable and can be tailor to the different users using the interactive map. Furthermore, the spatial data is organized in a catalogue that offers online access services such as WMS, WFS, KML or the download of files.
i3Geo was developed by the Ministry of the Environment of Brazil and it is actually part of the Brazilian Public Software Portal.
gvSIG Educa
What is gvSIG Educa?
“If I can’t picture it, I can’t understand it (A. Einstein)”
gvSIG Educa is a customization of the gvSIG Desktop Open Source GIS, adapted as a tool for the education of issues that have a geographic component.
The aim of gvSIG Educa is to provide educators with a tool that helps students to analyse and understand space, and which can be adapted to different levels or education systems.
gvSIG Educa is not only useful for the teaching of geographic material, but can also be used for learning any subject that contains a spatial component such as history, economics, natural science, sociology…
gvSIG Educa facilitates learning by letting students interact with the information, by adding a spatial component to the study of the material, and by facilitating the assimilation of concepts through visual tools such as thematic maps.
gvSIG Educa provides analysis tools that help to understand spatial relationships.
Definitely a site to visit if you are interested in open source GIS software and/or projects.
From the homepage:
i3Geo is an application for the development of interactive web maps. It integrates several open source applications into a single development platform, mainly Mapserver and OpenLayers. Developed in PHP and Javascript, it has functionalities that allows the user to have better control over the map output, allowing to modify the legend of layers, to apply filters, to perform analysis, etc.
i3Geo is completely customizable and can be tailor to the different users using the interactive map. Furthermore, the spatial data is organized in a catalogue that offers online access services such as WMS, WFS, KML or the download of files.
i3Geo was developed by the Ministry of the Environment of Brazil and it is actually part of the Brazilian Public Software Portal.
I followed an announcement about i3Geo 4.7 being available when the line “…an application for the development of interactive web maps,” caught my eye.
Features include:
- Basic display: fix zoom, zoom by rectangle, panning, etc.
- Advanced display: locator by attribute, zoom to point, zoom by geographical area, zoom by selection, zoom to layer
- Integrated display: Wikipedia, GoogleMaps, Panoramio and Confluence
- Integration with the OpenLayers, GoogleMaps and GoogleEarth APIs
- Loading of WMS, KML, GeoRSS, shapefile, GPX and CSV layers
- Management of independent databases
- Layer catalog management system
- Management of layers in maps: Change of the layers order, opacity change, title change, filters, thematic classification, legend and symbology changing
- Analysis tools: buffers, regular grids, points distribution analysis, layer intersection, centroid calculation, etc.
- Digitalization: vector editing that allows to create new geometries or edit xisting data.
- Superposition of existing data at the data of the Google Maps and GoogleEarth catalogs.
Unless you want to re-invent mapping software, this could be quite useful for location relevant topic map data.
I first saw this at New final version of i3Geo available: i3Geo 4.7.
From the webpage:
geometry-api-java
The Esri Geometry API for Java can be used to enable spatial data processing in 3rd-party data-processing solutions. Developers of custom MapReduce-based applications for Hadoop can use this API for spatial processing of data in the Hadoop system. The API is also used by the Hive UDF’s and could be used by developers building geometry functions for 3rd-party applications such as Cassandra, HBase, Storm and many other Java-based “big data” applications.
Features
- API methods to create simple geometries directly with the API, or by importing from supported formats: JSON, WKT, and Shape
- API methods for spatial operations: union, difference, intersect, clip, cut, and buffer
- API methods for topological relationship tests: equals, within, contains, crosses, and touches
This looks particularly useful for mapping the rash of “public” data sets to facts on the ground.
Particularly if income levels, ethnicity, race, religion and other factors are taken into account.
Might give more bite to the “excess population,” aka the “47%” people speak so casually about.
Additional resources:
Pan-European open data available online from EuroGeographics
From the post:
Data compiled from national mapping supplied by 45 European countries and territories can now be downloaded for free at http://www.eurogeographics.org/form/topographic-data-eurogeographics.
From today (8 March 2013), the 1:1 million scale topographic dataset, EuroGlobalMap will be available free of charge for any use under a new open data licence. It is produced using authoritative geo-information provided by members of EuroGeographics, the Association for European Mapping, Cadastre and Land Registry Authorities.
….
“World leaders acknowledge the need for further mainstream sustainable development at all levels, integrating economic, social and environmental aspects and recognising their inter-linkages,” she said. [EuroGeographics’ President, Ingrid Vanden Berghe]
“Geo-information is key. It provides a vital link among otherwise unconnected information and enables the use of location as the basis for searching, cross-referencing, analysing and understanding Europe-wide data.”
Geographic location is a common binding point for information.
Interesting to think about geographic steganography. Right latitude but wrong longitude, or other variations.
eSpatial launches free edition of mapping software
From the post:
eSpatial, leading provider of powerful mapping software today announced the launch of a free edition of their flagship mapping software, also called eSpatial.
eSpatial mapping software lets users convert spreadsheet data into map form, with just a few clicks. This visualization provides immediate insights into market trends and challenges.
The new free edition of eSpatial is available to anyone who signs up for an account at www.espatial.com. Once logged on, users can create maps from their existing data and then post them on websites as interactive maps.
Since it launched last year, eSpatial has made strong inroads into the sales mapping and territory mapping software market, especially in the United States.
Paid editions (including Basic, Pro and Team) of the application with greater functionality – including the ability to handle increased amounts of data, reporting and sharing options – start at $399 for an annual subscription.
Just starting playing with this but it could be radically cool!
For example, what if you mapped a particular congressional district and then mapped by zip codes the donations to their campaign?
I need to read the manual and find some data to import.
BTW, high marks for one of the easiest registrations I have ever encountered.
The State of Hawai’i Demands a New Search Engine by Matthew Hurst.
Matthew writes:
We will soon be embarking on a short trip to Hawai’i. Naturally, I’m turning to search engines to find out about the best beaches to go to. However, it turns out that this simple problem – where to go on vacation – is terribly under supported by today’s search engines.
Firstly, there is the problem with the Web Proposition. The web proposition – the reason for traditional web search engines to exist at all – states that there is a page containing the information you seek somewhere online. While there are many pages that list the ‘best beaches in Hawai’i’ as the analysis below demonstrates these are just sets of opinions – often very different in nature. An additional problem with the Web Proposition is that information and monetization don’t always align. Many of the ‘best’ beaches pages are really channels through which hotel and real estate commerce is done. Thus a balance is needed between objective information and commercial interests.
Secondly, beaches are not considered local entities by search engines. While the query {beaches in kauai} is very similar in form to the query {restaurants in kauai} the later generates results of entities of type
while the former generates results of entities of type . While local search sounds like search over entities which have location, it is largely limited to local entities with commercial intent. Finally, there is general confusion due to the fact that the state of Hawai’i contains a sub-region (an island) called Hawai’i.
…
As you may have guessed, had Matthew’s searches been successful, there would be no blog post.
How would you use topic maps to solve the shortfalls that Matthew identifies?
What other content would you aggregate with beaches?
Image compositing in TileMill by Kim Rees.
From the post:
TileMill is a tool that makes it easy to create interactive maps. Soon they will be adding some new features that will treat maps more like images in terms of modifying the look and feel. This will allow you to apply blending to polygons and GIS data.
BTW, a direct link for TileMill.
On brief glance, the TileMill site is very impressive.
Are you tying topic maps to GIS or other types of maps?
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