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Geospatial Data Unchained – India’s new policy guidelines on data collection, access and services.

On 15th February 2021, The Department of Science and Technology (DST) announced India’s geospatial mapping guidelines for Indian geospatial technology sector. Let me take a step back and try to attempt to outline how these new policy guidelines are envisioned to help and whom.

Survey of India (SOI), under the Department of Science & Technology (DST) is having special responsibility to survey and mapping of India to help integrated development. The same department has carried the legacy data access protocols over several decades which made the process of obtaining maps and geospatial data so much complicated. Often SOI itself has to sought permissions from various Government authorities to discharge their duties as the nature of the data is sensitive and confidential in nature by then.

Meanwhile, Indian Space Research Organization (ISRO) under the Department of Space (DOS) has proven its mettle in satellite launching for the benefit of several sectors and collected the wide range of remote sensing data with impressive resolution standards.

However, the same set of hardships are repeated to access the geospatial data from ISRO/NRSA/SAC. In case any private sector or research institute are in need of geospatial data for respective purposes, there are no standard guidelines or process in place to grant access and one should walk pillar to post to complete the required paper work for the same.

On other hand, same is the difficulty in conducting surveying and data collection using drones and LiDAR technologies over a known area which needed unknown set of permissions to be taken where there are no clear guidelines to help the needy. It not only pushed private sector’s commercial projects on the shelves but also affected the academic researches due to lack of access to proper data.

Over a period of time during the digital revolution, many cutting edge technologies have helped various sectors and seen many advancements towards BigData, Machine Learning, IoT and Digital Twin and eventually DATA become a new OIL.

There were few major setbacks Indian geospatial industry had faced and are not limited to,

  1. Lack of access to reliable and context specific geospatial data
  2. Lack of overall policy and guidelines for ease of access
  3. Lack of clear understanding of data sharing and storage policies
  4. Lack of access to Continuously Operating Reference Stations (CORS) network

Often perceived that geospatial industry moved in the back seat holding its feet tied up with complex geospatial policy guidelines. But, now that it is inevitable to realise the importance of geospatial data and also the data that India has, is already available globally and hence there is no holding the data in the name of confidential and privacy.

As part of the reforms, DST held a press release chaired by Union Minister Dr Harsh Vardhan, MoS, Dr Jitendra Singh, Ashutosh Sharma outlining the major objectives and guidelines on data acquisition, services and data sharing. Below are the major takeaways from the press release to liberalise and deregulating the way geospatial data has been made available and exchanged.

  1. The Survey of India (SoI) and ISRO who are surveying, collecting and maintaining Geospatial Data are directed to make the access procedure simplified and transparent to Indian citizens avoiding prior permissions and data licenses by using cloud technologies and open data APIs in various formats. Essentially moving away from complex approval process to self-certification and self-identification process.
  2. Any private, public and research institutes are entitled to data collection, processing, storing, publishing and sharing the geospatial data within India and using the same in India projects.
  3. Access to CORS network for real time positioning and their data shall be made available without any restrictions.
  4. Mobile Mapping, Street View survey and LiDAR sensors survey shall be permitted to any Indian private, public or research institutes irrespective of accuracy.
  5. Spatial accuracy of 1m for horizontal and 3m for vertical resolution spatial data is accessible without prior approvals for any known area.
  6. Any public digital or paper maps can have all kinds of geospatial features on the map however, labels and symbology are restricted over secured areas.

Location information has become an integral part of most of the businesses either the existing old business who adopted Geospatial technologies off late or the new startups which are unlocking economic, social and environmental opportunities for sustainable growth and development of the country. Apart from startups, traditional geospatial sectors such as Telecom, Defence, Mining, Oil, Transportation, Gas and Utility markets are expected to be benefited tremendously. This reform will surely put Indian Geospatial projects on world market and helps encouraging startup eco-system as well.

Fingers crossed on how effectively these guidelines on paper takes shape and make it reality to unchain the geospatial data for the needy in geospatial sector. Comments and discussions are appreciated as to exchange different perspectives on this reforms.

Source: Press Release, Media Briefing

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Choosing a Geocoder in 2021

Geocoding is a subject most geospatial users have to deal with on a regular basis. It is often one of the first steps in analyzing a dataset.  But rarely do we really think about the reasons why we choose one geocoder over another other than I know what I know, and I like what I like.  But reflecting on this thought I realized that I have made some very poor decisions on picking geocoders over the years, and I am making a resolution in 2021 to not repeat this mistake.

My History With Geocoders

While writing this article, I tried to write down every geocoder I’ve ever used over the years.  My Gmail account dates from 2005 so there are about 15 years of documentation in there for me to look at but even before Gmail I have been using Geocoders.  The less said about those built on Fortran I used in the 90s the better, but my first exposure to geocoding was with TIGER/Line in ArcView 3.x and ArcGIS 8.x.  Boy, our standards for accuracy were much lower back then, but it did teach me much about how a geocoder works.  

Once Google, Yahoo! and others released APIs in the mid-2000s, it made Geocoding much easier and gave us so many more choices including rooftop level, highly-accurate geocoding which really was a game-changer for many of us.  Over the past decade, I have probably used Mapbox and Google geocoders more than any others but as I mentioned before, I have not always been using the right tool for the right reason.

What I look for in a Geocoder

Traditionally I look for a good API, one that has an SDK for my platform/language and a pricing model I understand.  I probably use Google’s geocoder the most because I generally am building applications on Google’s mapping APIs.  In that case, the Google geocoder makes a ton of sense.  But I also have used Mapbox’s geocoder quite a bit when using their APIs.  If you had to make me choose which one I liked the most, I don’t think I would answer.  I always pick the geocoder which is for the platform I’m on.  But this leaves out the big issue I’ve been thinking about.

What should matter and when

The two big questions I didn’t think about were data portability and accuracy.  In my experience I focused on using the geocoder on the platform I was on, but as I’ve started integrating data in disparate platforms, displaying geocoded data on a specific mapping API becomes an impediment.  Having a Geocoder that is platform-agnostic is critical to my workflow these days as my data is all over the place.  The freedom to pick and choose now trumps my want of one platform.  

Secondly, accuracy has become a big deal to me.  At previous companies, I used rooftop geocoding for data that was neighborhood-level accuracy.  Essentially I’m paying for accuracy I don’t need or even want to imply.  Paying for an expensive geocoder when you only need this level of accuracy is a huge waste of money and in my case a waste of time.  

And one more thing

A final point to consider: when we talk about geocoding more people typically mean “forward geocoding”, and don’t pay much attention to reverse geocoding. But reverse and forward geocoding are different, and it doesn’t make sense to assume because a provider is good (or bad) at one, they are at the same level for the other. If you are wondering what reverse geocoding is all about check out OpenCage’s guide to reverse geocoding. Just like with accuracy you may be way overspending if you are paying a premium for a service that is great at forward geocoding, and then use it only for reverse. 

So which Geocoder am I using?

Well, I’m using them all.  The epiphany that I’ve come to doesn’t mean that I throw Google/Apple or Mapbox/Here out the door.  It just means that I now look at my use case before getting started and see what works best for my project.  It might be Google on Google Maps, Apple on Apple Maps, or Mapbox on Mapbox, but it also might be OpenCage on OpenStreetMap or OpenCage on Here or any other endless combination my clients might come up with.  Bringing in tools such as OpenCage which gives me lower cost, permissive licensing, no vendor lock-in. Multiple geocoders under a single API are giving me the freedom to focus on my data and not on the technological process of geocoding.  

Embracing more geocoders in my workflow has made my products better, my clients happier, and nothing beats saving money and time.

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