There is a wealth of ways to get imagery today from buying or downloading satellite imagery to hiring a company to fly your project, but it is the low cost, low effort DIY imagery that is the most exciting and fun. The picture to the right is one of many that is available from Flickr, and other photo sites, that was taken with one of these methods (in this case a kite). With that in mind, the new poll wants to know what technology you prefer to use or are interested in using to capture low altitude imagery. Head over and share your thoughts. If you have a method that isn’t listed leave a comment on this post and I will add it to the list.
In a day I thought would focus on new sensors (LDCM) I end up thinking about old sensors and the piles of hard copy historic aerial photos that are going unutilized in our digital lives (insert standard reference to Peter Morville’s Ambient Findability here). Head over to check out what looks to be a great tool for digital historians, cultural landscape folks, historic archaeologists, and others.
As you might imagine from our previous conversations on the podcast, we are just a little excited about the LDCM. NASA/USGS have released the first scene from the new sensor focusing on the Fort Collins area. Head over the NASA site to take a look at the scene and associated information. Hopefully we will hear more next week as the ASPRS annual conference hits Boston.
The Guardian UK online has a media section called, “Data Store: Show and Tell“, which true to its name uses visualizations to tell a story about data. According to The Guardian Data Store team, infographics and data visualization have become the language of the Internet because everyone has access to free tools that make it possible to visualize complex data. In the past few months they have shown, among others, visualizations of Italian election results, Twitter’s languages of New York mapped, and an animation of Britain’s new rail network.
Their recent Show and Tell is about “US Baseball stars immortalized in statue-explore our interactive map” that shows how The Sporting Statues Project at the University of Sheffield mapped every baseball statue in North America. The mission of the Sporting Statues Project is to record and research statues of sportsmen and women around the world. To date they have collected information on over 600 statues; 249 of them U.S. baseball statues. The interesting part of their website is not just the maps and data, but also that the project itself grew out of a “labour of love”. Like many GIS mapping database projects, the data was collected and mapped by people who have an interest in the topic, the geospatial skills to map it, and the desire to share that data with other interested users. They were able to use maps, posters, conference papers, and their website to show that what they were doing was about more than just a physical statue and points on a map, but connected to world history and current events.
The Data Store team mention a disclaimer several times that “Google have paid to sponsor this page but all editorial is overseen and controlled by the Guardian Datastore team.” Google and The Guardian Datastore have a close relationship. In 2012, they hosted a live Q&A debate event focusing on the role data has to play in policy making and transparency around international development and foreign aid. Google has sponsored other journalism events, including journalism skills conferences to educate the next generation of digital journalists.
New Hampshire has a new bill circulating through its legislature that would ban aerial photography by anyone who isn’t the government. They’ve apparently amended the ban in committee that changes some of the major concerns, but a lot still remain. The original bill include kite cams or any other form of aerial photography collection, but the amended ban has scaled that back. The focus seems to be upon drones and, oddly enough, arming drones. If the ban goes into effect, flying a drone would be a misdemeanor, with certain licensed exceptions. The bill also specifies that drones can only be used by law enforcement to collect data if they’ve received a warrant, and even then the information needs to be destroyed within 24 hours.
Drones and the legalities surrounding them are likely to dominate a lot of remote sensing legalese over the next few years. This may be the first such attempt at banning for non-governmental use, but I’m willing to almost bet real money it won’t be the last.
The 2013 IEEE GRSS Data Fusion Contest scientific challenge has been held annually since 2006. The Data Fusion Contest is organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) in order to educate and promote best practices in data fusion applications. It is comprised of two individual contests: 1) Best Paper Award and 2) Best Classification Award, users can participate in one or both contests. This year’s contest uses hyperspectral and LiDAR fusion datasets of the University of Houston campus and neighboring area.
The Best Classification Award results must be submitted between February 16, 2013 and May 1, 2013. The Best Paper Award manuscripts need to be submitted by May 31, 2013
2013 IEEE GRSS Data Fusion Contest winners will receive one 16GB WiFi iPad (provided by DigitalGlobe, Inc.), their results submitted for peer review to an IEEE-GRSS Journal, and attendance at the Data Fusion Technical Committees and Chapters Luncheon of the 2013 IEEE International Geoscience and Remote Sensing Symposium in Melbourne, Australia, in July 2013.
The American Geosciences Institute sent out a press release today about the release of their video series on geoscience on YouTube. The series is available as a playlist on the AGI YouTube channel. The video above is the first episode, Building the Planet, which actually starts off with a flight to collect AVIRIS data and a quick discussion of remote sensing and talks a little about ground-based Lidar later in the episode.
Python Scripting for ArcGIS is a new text from Esri Press by Paul A. Zandbergen (2013). It isn’t the first Python book for the geospatial community or even focused on ArcGIS, but it is the first that has the Esri logo on it. Much like other recent books on Geo/Python we have seen, it focuses on integrating an introduction to Python with the industry specific materials. As Frank mentioned when he highlighted the book in a previous podcast, this allows users to gain exposure to Python, but it doesn’t fall back on the (in my opinion) bad habit of most programming texts of spending half of the book on the language and concepts before even getting to the application in the specific area. There is a time and place for that approach in Python specific books. When you add another software library to a book, then use it from the get go.
The text is broken into four parts including 1) learning fundamentals, 2) writing scripts, 3) carrying out specialized tasks, and 4) creating and using scripting tools. As you can imagine each of these parts builds on the previous through the book fourteen chapters. Early chapters take advantage of Model Builder to help the reader get into Python through geoprocessing tools, but by Chapter 4 the focus is on building and running code. The book comes with a DVD which includes data and code samples so that you can use the same data and code that the authors are running.
If you are looking to learn Python for use with your ArcGIS workflow, or a reference on the topic, this book is a good option for a growing bookshelf on the topic. The fact that you are using both Python and ArcGIS all the way through the book gets our support. With an MSRP of $79.95 and a current Amazon price of $48.45 the cost puts it in the range of similar books.
The Telegraph recently published an article, “How Supermarkets Prop Up Our Class System” by Harry Wallop introducing his book “Consumed: How Shopping Fed the Class System“. In the article, he discusses how marketers use census data and other location based data to aggregate postcodes into 60 different social groupings that they then repackage and sell back to retailers who use the analysis to micro-target potential shoppers. He believes that instead of creating more opportunities for shoppers, spatial targeting is reinforcing class stereotypes and creating structural inequality.
Geospatial marketing for supermarkets and grocery stores is growing in popularity for industry and public health. The Food Trust documented how Pennsylvania is using geospatial and GIS to target underserved communities for Penn State Public Broadcasting’s Geospatial Revolution Project. Job search databases advertise for positions such as geospatial marketing facilitator, interactive marketer, and geospatial marketing analyst. The Shopper Marketing trade journal lists mobile applications, QR codes, location based shopping, and augmented reality among the trends it uses to both reach and collect data from shoppers.
In today’s society it is difficult for shoppers to take advantage of grocery deals without providing personal information. A LifeHacker article on saving money, “Use “Jenny’s Number” to Get Club Discounts at Stores without Providing Personal Information” jokingly suggested trying to use the phone number from the popular 80′s song. Which semi-seriously raises the question of which social grouping the people who provide her number would fall under or how many shoppers give fake geospatial data.