For a change, this is not a subject-specific post, but a more general catchup on things mappy and not-so-mappy that we’ve been doing. In brief, the three of us who run this blog are all now wearing different hats from the ones we had a few months ago. The massive change in our lifestyles is that we’ve gotten official grown-up jobs, which is one of the reasons this blog’s been a bit quiet in the recent past. We can but hope that that will change as we get settled into our new jobs.

Sajjad is now with Mapbox in Bangalore, Sumandro is Research Director with the Centre for Internet and Society, in Bangalore and Delhi, and I’m Coordinator for GIS and Spatial Analysis at WWF-India. These are, to be honest, rather predictable roles; we started this blog because, other than the fact that we get on rather well, we thought that our shared interest in all things spatial, informed as it was by our different backgrounds in programming*, social policy research* and conservation, would make for an interesting collaboration.

I’ll let the other two talk about their day-to-day work with maps; for now, I’m going write a bit about the work I do. I’m part of the Species and Landscapes team at WWF-India, which consists of ten landscapes across India. As part of the team in Delhi, I’m helping coordinate the spatial information needs for all the landscapes. I’ve been actively involved with a couple of the teams before (the Western Himalayan and Western Arunachal Landscapes), and am looking forward both to meeting old friends and visiting new regions.

Today, I’m working on analysing some human-wildlife conflict data for villages near Ranthambore Tiger Reserve, cleaning up a subset of the recently released 1-arcsecond SRTM data (which is very nice but has voids that need filling) and collecting data on an infrastructure project in Uttar Pradesh that may affect wildlife corridors in the Terai region.

I’m planning a few posts over the next couple of months; one, which has been pending for a while, is a descriptive piece regarding a balloon-mapping aerial photography project I conducted with the Cambridge University Spaceflight Society. Another’s on the connection between the Indian Forest Department’s administrative boundaries and their hierarchy, and there’s also a long-pending post on Indian administrative boundaries, where strange animals like the tehsil and taluk are to be found. Finally, I have the beginnings of a blogpost about the importance of high-quality vector files of protected areas. And this list is only mine; Riju and Sajjad have their own set of blogposts to write.

Speaking of which, both of them will be at the OpenDataCamp in Bangalore next week; I’m not going to be there (since I have work!) but it’s been a great experience with really interesting people every year so far, so if you’re interested in maps and data, you should go too!

*Riju, Sajjad: I wasn’t sure how else to describe your respective interests in 3 words or less, so please feel free to (choose one: edit/get me to buy you a drink) when we meet next.

I’ve had Nanda Devi and the Sanctuary surrounding her in my thoughts for a very long time, and she seemed like a fitting first attempt to bring spatial data out of the digital world and into reality. For the uninitiated, Nanda Devi is a mountain in the Indian Himalaya, and she’s always referred to as she: the goddess in the clouds. Surrounded by a protective ring of mountains, she towers over them all, and this space between the ring and the central peak is known as the Nanda Devi Sanctuary. Due to this ring, the first entry into the Sanctuary was only made in 1934, by Shipton and Tilman and their three porters, who entered via the gorge of the Rishi Ganga; the mountain herself was first summited in 1936 (see- Nanda Devi: Exploration and Ascent, by Shipton and Tilman).

The geography of the region is fascinating ( and the history as well; there’s a nuclear-powered CIA device somewhere inside the Sanctuary!) and the heights and depths of the various relief features make it a joy to visualise. In this post, I’m going to describe, in brief, the steps I used to get from the data to the final model in wood. While I’m sure most of this can be done using open-source tools, as a result of my current University of Cambridge student status and my @cammakespace membership, I have access to (extremely expensive) ESRI and Vectric software, which I’ve used liberally.Relief map of the Nanda Devi Sanctuary and the Rishi Ganga gorge (dark->light = low->high)

I have a repository of digital elevation data collected by the Space Shuttle Endeavour in 2000 (STS-99; Shuttle Radar Topography Mission). It’s freely available from CGAIR-CSI (http://srtm.csi.cgiar.org/) and is not difficult to use. In QGIS, it was cut and trimmed down to my area of interest around Nanda Devi; I was looking for a rough crop that would include the peak, the ring and the Rishi Ganga gorge. This relief map was exported as a GeoTIFF, and opened up in ArcScene, which is ESRI’s 3D cartography/analysis workhorse. ArcScene allowed me to convert the raster image into a multipoint file; as the tool description states, it “converts raster cell centers into multipoint features whose Z values reflect the raster cell value.” For some reason, this required a lot of tweaking to accurately represent the Z-values, but I finally got the point cloud to look the way I wanted it to in ArcScene.

The point cloud (red dots), overlaid on the relief map in ESRI ArcScene

I then exported the 3D model of the point cloud in the .wrl format (wrl for ‘world’) which is the only 3D format ArcScene knows, and used MeshLab, which is an open source Swiss-knife type tool for 3D formats, to convert the .wrl file into a stereolithographic (.stl) file which the next tool in the workflow, Vectric Cut3D, was very happy with. As a side note, Makerware was also satisfied with the .stl file, so it is 3D-print ready.

The CNC router-ready model in Vectric Cut3D

More tweaking in Cut3D to get the appearance right, and the toolpaths in order, and I was ready to actually begin machining. After an abortive first attempt where the router pulled up my workpiece and ate it, I spent some more time on the clamping for my second attempt. First, I used the router to cut out a pocket in a piece of scrap plywood to act as my job clamp; this pocket matched the dimensions of my workpiece exactly. After a bit of drilling, I had my workpiece securely attached to the job clamp, which was screwed into the spoilboard on the router.

The CNC router doing its thing

For the actual routing itself, I used two tools; a 4mm ballnose mill and a 2mm endmill for the roughing and finishing respectively. It took about 45 minutes for the CNC router to create this piece. I love the machine, and am very grateful to the Cambridge Makespace for the access I have to it.

The final product

In the near future, I’m going to try and use different CNC router tools and types of woods to make the final product look neater; specifically, a 1mm ballnose tool for the finishing toolpath would be very nice! I’m also going to try and make relief models of a few other interesting physical features. While I am happy with this initial representation of Nanda Devi, if you have any suggestions as to improvements for future work, I’d be very happy to hear about them! I’d especially like to know if there are any open-source tools out there that can replicate the steps I needed to use ArcScene and Cut3D for.

I’ve re-entered the academic world as a student at the University of Cambridge in the United Kingdom, and one of the benefits I’m enjoying the most is near-unlimited access to one of the world’s largest repositories of recorded information; the Cambridge University Library. Commonly known as the UL, this is a copyright library which means that under British rules on legal deposit, the library has the right to request a copy of any work published in the UK free of charge. Currently, the UL has over 8 million items, which includes books, periodicals, magazines and of course, maps.

 

The Map Room in the UL is a fascinating place; it functions as the reading room for the Map Department, which holds over a million maps (as the librarian told me; Wikipedia claims it has 1.5 million). It’s not a very large room, as reading rooms go, but is a beautiful space and is very well managed. Everything is catalogued very efficiently with a filing card system, and there’s one card with the name, date of publication and classmark (UID/coordinates) for each map.  Visitors are not allowed to simply browse through the map collections; to refer to a map, one must fill out a request form with the appropriate details and submit this form to the library assistants, who will then pull out the required map folio from its storage location. The title of this post comes from the fact that  map holdings with classmarks beginning with ‘S696′, ‘Maps’ or ‘Atlases’ are held in the Map Room, in various drawers and cabinets.

 

The Map Room is a pen-free zone; if you’re writing something, use a pencil. Smartphones and hand-held cameras are allowed, but under UL policy photos cannot be taken of the building itself. With prior permission however, it is possible to take images of material in the UL, which I did. The first series is from a map on display in the UL; titled “A map containing the towns villages gentlemen’s houses roads river and other remarks for 20 miles around London“, it was printed for a William Knight in 1710 and is a wonderful piece of cartography. The second series is from a map I requested using the card-index system; this map dates back to 1949 and beautifully illustrates tea-growing regions in the Indian-subcontinent.

 

If there’s a map in the UL you want an image of (for non-commercial or private-study purposes only!), I’d be happy to do what I can to help; I would actually be very grateful for an excuse to spend an afternoon looking at maps.

IMG_6037
Detail from Knight, W. (1710). North Arrow.

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The Thermal InfraRed Sensor (TIRS) is a new instrument carried onboard the Landsat 8 satellite that is dedicated to capturing temperature-specific information.  Using radiation information from the two electromagnetic spectral bands covered by this sensor, it is possible to estimate the temperature at the Earth’s surface (albeit at a 100m resolution, compared to the 30m resolution of the other instrument, the Operational Land Imager).

I used data from the TIRS to estimate the surface temperature in the city-state of Delhi, India as of the 29th of May, 2013.  The relevant tarball file containing the data was downloaded using the United States’ Geological Survey’s (USGS) EarthExplorer tool; the area of interest was encompassed by [scene identifier: path 146 row 040] in the WRS-2 scheme. I think I’ll leave the specific explanations describing WRS-2, path/row values and the other miscellaneous small data-management operations for a later post. For now, I’ll let it be understood that these are important things to know when in the process of actually obtaining this data. When the tarball is unpacked fully, the bands from the TIRS instrument are bands 10 and 11;  the relevant .tif files are [“identifier”_B10.tif] and [“identifier”_B11.tif], and these were clipped to the administrative boundary of Delhi. There’s also a text file containing metadata: [“identifier”_MTL.txt] is essential for the math we’re going to do on these two bands.

 

Delhi as seen by Landsat 8 Band 10 (TIRS)
Delhi as seen by Landsat 8 Band 10 (TIRS)

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This will be a relatively short post; I’ve been working with Landsat data for a few years now, and I find it absolutely fascinating. The new Landsat satellite, initially named the Landsat Data Continuity Mission and now known as Landsat 8, is actually the 7th in the series; Landsat 6 never made it to orbit. When Landsat 8 was launched on the 11th of February 2013, I was really anxious and excited and when it made it to orbit successfully, I was ecstatic. I downloaded my first set of Landsat data (Path146/Row040, covering the Indian city of Delhi) off the USGS EarthExplorer website last week, and have been tinkering with it ever since.

 

State_of_Delhi_L8_imagery

 

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I was employed as a spatial data and cartographic consultant on a project to analyse specific agricultural commodities and Agricultural Produce Marketing Committees (APMCs) in the Indian states of Karnataka and Madhya Pradesh. The final product was a set of maps for various publications, as well as the clean datasets themselves.

Agricultural market datasets for the states of Karnataka and Madhya Pradesh were obtained for the purposes of spatial visualisation; these contained information on wheat procurement in Madhya Pradesh (2008 – 2012), tuar production in Karnataka (2007 – 2009) and the locations and categories of APMCs in both these states. Some of the data was linked to district names, while the rest was geocoded using a free online geocoding service. I used Quantum GIS, TextEdit and Microsoft Excel extensively for this project; Excel and TextEdit are invaluable when processing CSV files, and QGIS is where all the actual mapping itself takes place.

The actual process itself involved lots of data-cleaning and a little bit of mapping. First, for the geocoding, I ran the column containing the village names through the geocoder thrice; at each repetition, I tweaked the names a little more to get more accurate coordinate results. I then had to similarly tweak the district names to get them to match up with my source shapefiles; fixing bad spellings can be a LOT of work. In its entirity, this was a tedious process that involved organising, cleaning and validating four distinct datasets with both automated and manual operations. However, the final products were datasets that were clean, had accurate spatial locations and could easily be used to produce analytically valuable maps.

CASI _ Five years of wheat procurement in Madhya Pradesh _ Animated

 

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