Category Archives: Data

Demonetisation with Srinivasan Ramani

Srinivasan Ramani is Deputy National Editor who works with data at The Hindu. He has been a long time member of DataMeet community. This week I caught up with him to talk about Demonetisation move by Government of India.


Show Notes

Crossposted.

Happy Independence Day and Open Indian Village Boundaries

One of the longest and most passionately discussed subject on the Data{Meet} list is the availability of Indian Village Boundaries in Digital format. Search for Indian Village shape files and you can spend hours on reading interesting conversations.

Over last two years different members of community have tried to digitize the maps available through various government platforms or shared the maps through their organizations.

A look at the list discussion tells you that boundaries of at the least 75% of the states are available in various formats and quality. What we need at this point is a consolidate effort to bring them all on par in format, attributes and to some level quality. So some volunteers at Data{Meet} agreed to come together, clean up the available maps, add attributes, make them geojson and publish them on our GitHub repository called Indian Village Boundaries.

Of course this will be an on going effort but we would love to reach a baseline (all states) by year end. As of now I have cleaned up and uploaded Gujarat. I have at the least 4 more states to go live by month end. Karnataka, Kerala, Tamil Nadu and Goa. I will announce them on the list as they go live.

The boundaries are organized by state using state ISO code. All the village boundaries are available in geojson (WGS84, EPSG4326) format. The project page gives you the status of the data as we clean and upload. Data is not perfect yet, there could many errors both in data and boundaries. You can contribute by sending the pull requests. Please use the census names when correcting the attributes and geojson for shapes. Please source them to an official source when sending corrections.

Like everything else community creates. All map data will be available under Open Data Commons Open Database License (ODbL). This data is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. If you find issues we are more than happy to accept corrections but please source them to an official source.

On this 70th Independence day, as we celebrate the historic event of India becoming Free and Independent, Data{Meet} community celebrates by cleaning, formatting and digitizing our village boundaries. Have a great time using the maps and contributing back to society.

https://github.com/datameet/indian_village_boundaries

Picture: Kedarnath range behind the Kedarnath temple early morning. By Kaustabh, Available under CCBYSA.

Sikkim Data Portal and Sensitive Information

Sikkim was the first state to come up with its own Sikkim Open Data Acquisition and Accessibility Policy (SODAAP) on the lines of National Data Sharing and Accessibility Policy (NDSAP).  Continuing to lead Sikkim is now officially the first state to have its own data portal we are really happy to see this development and hope more states follow.  DataMeet has been carrying consultations with officials of Sikkim in framing the policy and helping them with workshops and insights to use the data. Honorable Member of Parliament Dr. Prem Das Rai has also been our keynote speaker during the Open Data Camp 2015 at Delhi sharing experiences about the on-going work in Sikkim.

As emails were being pushed about the launch of the portal on 15th July, we were alerted about sensitive data being published through the data portal by Abhay Rana. Two datasets on the portal had sensitive information like 1) name, 2) religion, 3) caste, 4) father’s name, 5) mother’s name, 6) gender, 7) birth date, 8) residential address, and 9) information regarding disabilities (if any) of school children, teachers with additional detail of marital status for the teachers.  We alerted both NIC and the chief data officer in charge for the datasets to get them taken down immediately.  Open data does not promote any sensitive information being shared publicly and it violates the very core principles. We applaud the quick response by the data controller in response.

It was an unfortunate accident that sensitive information not to be published under the policy was shared through the data portal. NDSAP along with SODAAP has mandates for every department to make sure sensitive information has restricted access and is not to be published. This incident is not the first where we encountered sensitive information was being published by government officials. Most of the times such information is in the public domain by accident or due to lack of awareness among officials about type and parameters available under the datasets. More incidents like this can harm officials from publishing further data and is a threat to the ecosystem of open data.

As more and more data becomes part of the public domain it is important that we all can work together to ensure that we do not violate privacy or put up sensitive data. More guidelines and frameworks are needed to maintain and report sensitive data which is already public.

We request you to bring to our attention if any sensitive information is being published under the pretext of open data. For now explore the new data portal and use open data to bring positive change in your community.

BMTC Intelligent Transportation System (ITS) and need for Open Transport Data

Bangalore Metropolitan Transport Corporation (BMTC) has recently launched its Intelligent Transportation System (ITS) in May’16. First announced in 2013, this was one of the systems most data enthusiasts in urban transport were eagerly waiting for. The system was designed to scale on paper and BMTC made sure the data rights of data being generated are with them instead of the contractor. Even with extreme planning, the system was delayed by 2 years and has several issues with it. Some of these issues have been highlighted to BMTC by members of datameet’s transport working group with suggestions to make it better in early June.  Along with the suggestions we had several questions regarding the project, we have asked BMTC to help us understand the ITS system in a better way and expressed interest to be part of the Evaluation & Monitoring (E&M) of the ITS project.  It is important that the project is closely monitored to improve public transportation for Bengaluru.

 

We also shared some of the previous work carried out by members of the group and suggestions to use open transport standards like GTFS, usage of openstreetmap data to reduce maintenance costs for currently using third party services like Google Maps which is not entirely free.

Members of datameet have been working on transport data of BMTC since 2010. Thejesh GN hosts static data of routes and schedules  of various years through his project OpenBangalore.  As a community of researchers, data users and enthusiasts we have been studying and experimenting with the evolution of data practices in India. Open Data is helping us be aware of our surroundings and also contribute back to the city in our own way. BMTC’s ITS implementation is a opportunity for most of us, we can potentially use GPS data to understand traffic patterns, rash driving of bus drivers, skipping of bus stops and trips. The ITS system will help the commuters more than ever if being utilized the right way. Open Data can help make this dream a reality by letting any commuter analyze his ride. Officials of BMTC has made announcements of bringing up a data sharing policy on the lines of National Data Sharing and Accessibility Policy (NDSAP). In this regard we requested them to host a public consultation for their draft data sharing policy. We hope we can help BMTC and Bengaluru in a better way by bringing a policy suitable for all commuters and not just data users.

 

Open Access Week 2015

Late post

Open A20151024_190330ccess Week is used as an opportunity to spread awareness of open access issues throughout the world. It was Oct 24th to the 30th last year. Shravan and Mahroof from the Ahmedabad Chapter suggested we do the first every multi city hangout and bring together different groups working on openness issues throughout the country.

For the event we had a Google Hangout with:

Data.Gov.In started us off with  Alka Misra and Sitansu participating from Delhi. They spoke about new features on Data.Gov.in, new datasets and visualizations available. They were also there to extend invites for more participation from the community.

Rahmanuddin from Access to Knowledge then spoke about Wikipedia and their community dedicated to local language knowledge sharing. They also had pertinent questions to Data.Gov.In regarding using open licenses. Since Wikipedia can’t use any data from Data.Gov.In since a license isn’t specified.

Ahmedabad Chapter went next. Ramya Bhatt, Assistant Municipal Commissioner from Ahmedabad, came and gave a brief talk about their plans for open data and smart cities. Alka from Data.Gov.In offered assistance. Then some students from Dhirubhai Ambani Institute of Information and Technology’s machine learning program used some data from Data.Gov.in to do analysis at the event. They looked at high budget allocation per state and drop out rates.

Open Access India’s Sridhar Gutam briefly went through the plans OAI has for the upcoming year to promote open access science and journals.

Hyderabad DataMeet is a new and yet to really take shape meet up but we were happy to see a first attempt. Sailendra took the lead as the organizer and brought together some people from IIM Hyderabad. Srinivas Kodali was there to talk about all the data he had made available that week.

 

20151024_184755Banalore DataMeet was there to share what has been going on with DataMeet and any new iniatives in Open Access

 

 

It was a great event, and as with all online events there were some technical difficulties but everyone was patient. It was awesome to see how the open culture space has grown, and to see so many new DataMeet chapters.

You can see the event below:

I hope we do one again soon minus the technical difficulties.

GPS and its Discontents

There is no greater success story for open data than GPS. The decision by the US government to make it available so it can be used for commercial purposes is the stuff of lore and what propels so much of the enthusiasm for open data.

Audiomatic’s show The Intersection is a podcast hosted by the dynamic duo Padmaparna Ghosh and Samanth Subramanian who explore interesting topics every other week.

Last week they did a show about GPS and it’s history and uses. Our own Thejesh GN was interviewed about his hobby of using GPS to go on treasure hunts.  They also talk about the Indian Government’s move to create a national GPS infrastructure with their own satellite so they don’t have to rely on the US.

I found the podcast informative and interesting and it hit on an important note as to why open data in India is so important.

Like GPS infrastructure to support India’s defense; data in India also needs to be invested in and promoted so that the reliance on others can reduce. Why is Google Maps, not Survey of India,  the source of mapping information in India? Why are their so many private data collection networks set up with foreign funds and private interests?Because GOI doesn’t invest in the potential of their data to build markets and make their job easier and more effective.

Open data is just one way of showcasing how better data can be used as well as offer guidance on how the government can invest in data collection and dissemination.

Anway it is a great podcast please give it a listen.

Analysing Bangalore’s Bus Network

Open Bangalore has been a pioneer in opening up several data sets that help understand Bangalore city. This includes the network of Bangalore Metropolitan Transport Corporation (BMTC). The BMTC operates over 2000 routes in the city and region of Bangalore and is the only real mode of public transit system in the city. Some of us at DataMeet took to time understand its network better by performing some basic analysis on the gathered dataset. The data set had bus stops, routes and trips. We inspected frequency, coverage, redundancy and reachability.

Longest route

BMTC is known for its many long routes. Route 600 is the longest, making a roundtrip around the city, covering 117 km in about 5 hours. There are 5 trips a day, and these buses are packed throughout. It should be noted that while the route traces the edges of the city in the west and north, it encircles the larger industrial clusters of the east and south.

View the map full screen.

Frequency

Next, I wanted to look at the frequency of different routes. In the image below, stroke thickness indicates how many trips each route makes. The relationship of the bus terminals with neighbourhoods and the road network can be easily observed. For instance, the north and west of the city have fewer, but more frequent routes. Whereas, the south has more routes with less frequency. Also, nodes in the north and west seem to rely more on the trunk roads than the diversely-connected nodes in the south. One can easily trace the Outer Ring Road, too.

View the map full screen.

Reachability

I tried to define reachability as destinations one can get to from a stop without transferring to another bus. The BMTC network operates long and direct routes. The map shows straight lines between bus stops that are connected by a single route. The furthest you can get is from Krishnarajendra Market (KR Market) to the eastward town of Biskuru: roughly 49 km as the crow flies.

View the map full screen.

Direction

Which directions does BMTC run? It is interesting that BMTC covers the city North – South (blue) and East – West (brown) with almost equal distribution.

View the map full screen.

Coverage

BMTC routes are classified into different series. Starting from 1 – 9 and A – W. I analysed coverage based on series 2 (blue) and 3 (green) and they make up almost 76% of the entire network.

View the map full screen.

Redundancy

Tejas and I took turns to try and figure out the redundancy within the network. Redundancy is good to absorb an over spill of bus commuters. Redundancy is a drain on resources and makes it hard to manage such a vast network with efficiency. So, we looked at segments that overlapped different bus routes.

View interactive map.

Node strength

This map by Aruna shows node strength – number of routes passing through a particular stop. You can see that the strength decreases as we move away from the city center with the exception of depots.

View interactive map.

Just like the data, our code and approach are open on Github. We would love to hear from you, and have conversations about the visualization, the BMTC, and everything in between!

Global Open Data Index: Water Quality

Last year I helped assess the water quality section of the Global Open Data Index (GODI). Given the news of lead poisoning in Flint, Michigan and increasingly beyond, safe drinking water is no longer assured even in countries where it’s been guaranteed, so I am very glad they included it in GODI.

GODI is a survey of 122* countries that look at the status of ‘high priority datasets’ and whether they are truly open according to the Open Data Criteria. Water quality was included last year for the first time. So my job was to examine each country’s submission  and assess if the data submitted was what was asked for and met the criteria for being open. This was a daunting task but I figured if I could find water quality data in India of all places it wouldn’t be impossible.

Assessment Criteria/Methodology

GODI looked for very specific parameters:

While there are a lot more parameters that could be asked for, these were a good sample of parameters to assess if there is robust water monitoring in the country.

After the initial submission phase there were a lot questions about why wouldn’t the survey just ask for drinking water quality data or environmental monitoring data?

Choosing parameters instead of programmes is important because monitoring the environment and drinking water quality are connected. Some countries haven’t really established large nationalized water treatment strategies, drinking water comes directly from a natural resource so the environmental monitoring data inadvertently applies to the drinking water scenario.  Which means that if a country really has robust water quality data they must have these 5 parameters because they cover surface and ground water sources and also reflect safe drinking water standards.

The assessment would be rejected if a submitter only found the surface water body monitoring stations (environmental water monitoring) for instance because arsenic and fluoride are only found in groundwater. So the submitter would either ideally find the treated drinking water quality data which will cover all the parameters or the source water quality data for both surface and ground water.

For a full look at the methodology of the entire survey go here.

Some background

There is no one way to create water management systems but there are two major ways by which people get water – directly from the source or piped in from a source or a treatment facility. The origins of the water source is important. If you are getting water from the ground there are different quality issues  than from surface water (lake or river). If water is from a treatment plant there is a possibility that plant is getting water from both surface water, ground water, and in some cases recycled water. Usually water quality is measured at source and after treatment (treatment plants take multiple water quality samples during the treatment process.)

A full water quality assessment means lots of parameters and not all of them are tested the same way; some parameters take several days and require specific conditions, others can be taken easily through filters or litmus papers.  Water quality is a deliberate process of sampling and testing, and it not as easy as sticking a sensor into the water and monitor a continuous feed of data (although the potential for these approaches is quickly growing as technology improves.)

What I looked for

Since water quality was a scientific process I figured if I found any proof of water treatment or quality monitoring, a dataset would not be far off. After going through a few countries I noticed that the different water management approaches and policies affected where you would find the data.

Most countries give drinking water treatment responsibilities to local bodies but sometimes is monitored by central government under public health regulation so aggregated data could lie with the public health ministry or the environmental protection body.  In most cases responsibility for environmental monitoring fell to a central government Environmental Ministry.

So this scenario means that multiple datasets exist – a centralized dataset for surface and groundwater that  usually lies with the environmental ministry that could have all the parameters but sometimes doesn’t, or it doesn’t have real time data (this means data  may be available but from less frequent data collection such as quarterly or half yearly efforts). Or the Public Health Ministry has reports of water quality with all the parameters but these are aggregated, and usually in a report form (not a dataset) and not updated in a timely manner.

The US, for instance, falls under this group and can produce confusing submissions. The US has a robust geological survey of surface and ground water sources. However, the drinking water reports are supposed to go to the Environmental Protection Agency but no one seems to be updating the database with information. In my assessment I reduced the score because both are supposed to be available in the public domain.

There are countries like Belgium where water management and monitoring are completely left to the local body and there is no central role for monitoring at all, which meant there is no dataset.

There are countries where there is a strong central role in water management and a dataset could be made open like in France. Korea stood out, because they have live real time water quality information from their treatment plants that gets updated to a website.

Then there are the ‘unsures’: which are countries that seem to treat water to some degree or have national drinking water monitoring programmes but don’t have data online, reports or any mention of data at all. This is not restricted to the developing world. I was very frustrated with several European countries with newspaper articles riddled with reports of how pristine and delicious their water is that don’t have a single public facing dataset.

Take Aways

United Kingdom and the US, both pioneers of the open data movement had terrible water quality data for water treatment, and no effort has been made to bring the data together or make it available in a real time fashion.  Also it is not clear to citizens who holds local bodies accountable for not updating their reports, making reports public or finding ways to bring this data into the light so it can be usable. It is no wonder that the US is now on the cusp of a public health crisis.

It is frustrating that the open data movement hasn’t quite been able to reconcile decentralization and local responsibility with national level accountability and transparency. Public health is a national level issue even though local and regional contexts are required for management. How do we push for openness and transparency in systems like this?

In places like India where water quality treatment is largely left to private players and huge populations are not receiving treated water, the need for data to be available, open, and in the hands of central bodies but also local players is a must, because people need to try to find solutions and where to intervene. Given the huge problems with water borne diseases, the slow but epic arsenic and fluoride poisonings gripping parts of India, and the effects this will have for generations, making this data public, usable and demystified is no longer an option.

All in all, I have to say this was an enlightening experience, it was cool to be able to learn something about each country. In our continuous push for open data we sometimes get lost in standards, formats, and machine readability, but taking a moment to really prioritize our values in society and have open data reflect that is essential. Public health outcomes and engaging with complex issues like it are an essential part of how to grow the open data movement and make it relevant to millions more.

*(Correction: Previous version said the survey included 148 countries, the actual number is 122.)

Bihar Elections

DataMeet has always been interested in doing projects so last year we decided to run a pilot. In the last few years the demand for data work has increased from non profits and journalists and they usually approach data analytics vendors like Gramener. However, these firms can be expensive or have high paying clientele which means that smaller accounts tend to not get their full attention. This leads to an increase in volunteer events like hackathons which don’t always result in finished usable products or can give non profits the long term engagement they need to solve issues. Vendors are not usually privy to the specific data problems a sector has and don’t want to let their tech people invest the time to learn about the subject and understand the particular data challenges. Though the civic tech space is growing, non profits and media houses can’t yet afford or see the need for internal tech teams to deal with their data workload.

With all this in mind we wanted to see if DataMeet can help fill and enrich this space as well as help build capacity within non profits to manage data projects. We were trying to find out, can we assemble teams through the DataMeet network to manage the entire pipeline of data work from clean up to visualization. These wouldn’t be permanent teams but filled with freelancers or hobbyists.

For this first project DataMeet would project manage and Gramener would provde the data analysts, the non profit managing partner was Arghyam and the ground partner was Megh Pyne Abhiyan. Megh Pyne Abhiyan works in several districts in north Bihar on water and sanitation issues. They wanted to use data to tell the story of what the status of water and sanitation was in those districts as a way of engaging with people during the election. It was decided we would do water and sanitation (WATSAN) status report cards for 5 districts — Khagaria, Pashchim Champaran, Madhubani, Saharasa, and Supaul — using government data.

This was an exciting project for us because it would be the first time DataMeet would work with a partner who works on the ground and the output would be for a rural, non online, non-English speaking audience.

DataMeet would project manage the process of data cleanup, analysis and visualization (which the team from Gramener would do) and then give the report cards to the Megh Pyne Abhiyan for them to do the translation and create the final representation of the report cards for their audience.

The Data

The partner wanted the data to be mapped to Assembly Constituencies, they wanted analysis for following situations

  1. Sanitation coverage for each Assembly Constituency and Gram Panchayat.
  2. Water quality, what is the contamination situation of the district, Assembly Constituency and Gram Panchayat.
  3. Water access, how do people get their drinking water.

It was also important to understand this data in the context of the flood prone areas of Bihar. For instance if there is an area that gets drinking water from shallow wells, with little sanitation in a high flood area those areas can suffer from high levels of water borne diseases.

The data we got was from

Since we were doing report cards based on Assembly Constituencies we needed the data to be at the Gram Panchayat (GP) level. Luckily the MDWS does a good job of collecting data all the way down to habitation so GP level data was available.

There is no official listing of what GPs are in which Assembly Constituency so the partner was asked to split the data by AC so we wouldn’t have to do that mapping. They agreed they knew the area better and would have the resources to pull together all the GP level data into organized Dropbox folders grouped by districts then split into ACs.

Data Cleanup

We received one PDF file per GP,  for water access and number of toilets, water quality was given in one large file by district.

All the data we received was in PDF. This was a huge hurdle as the data was from the government information management system so it was from a digital format but rendered in a PDF this meant that we had to convert unnecessarily. However, since the ground partner picked the data they needed and organized it by AC we wanted to make sure we were using the data they specified as important. So we decided to convert the data. This job was done by Thej and I and was extremely manual and time consuming and caused some delay in the data being sent to the analysts.  (See how we did it here.)

Analysis

The analysis required was basic. They needed to know at an AC level what the sanitation coverage was, the sources of water, how people were accessing it and what the water quality situation is.  Rankings compared to other districts and ACs were done to give context. Rankings compared to other districts and ACs were done to give context.So in all the analysis stage didn’t take much time.

Example of Analysis

 

Visualization

The UNDP along with the Bihar State Disaster Management Authority had created a map of diaster prone areas including flood. It was in PDF so we asked the folks at Mapbox India to help out with creating a shapefile for the flood map so we layer flood areas onto the Assembly Constituencies.

Bihar AC map with flood prone areas

 

While we had AC maps we didn’t have GP level maps. They didn’t seem to be available and we couldn’t find them in PDF form either.

Since the election is staggered by district we started with Khagaria. After the initial report cards were done the partner wanted just the cleaned up data in tables to use for their meetings. So we then decided to do the report cards, clean up the data and send the spreadsheets over to them.

As we were processing the next 4 districts I found GP level maps of Bihar, with boundaries of ACs included. This was quite exciting and I thought since we had some time we could do maps for the four pending districts.

After receiving the analysis for the next district I decided that since it would take to long to trace the PDF maps, so the analysts could map the GPs, I would just over lay them onto our AC shapefiles in Photoshop. I was going to put icons or circles in the center of the GP and that would be the map. While tedious I figured it would be worth it to show the maps to the ground partner.

However, when I started mapping I realized that analyzed data wasn’t matching up with the GPs on the map. The GPs listed in the Assembly Constituency in our original folders were incorrect, which meant all the analysis was wrong. Everything had to be checked against the maps and reorganized in the final datasets and then reanalyzed. This caused a huge delay.

On top of that the GPs on the map were spelled differently than in the MDWS data, and every dataset potentially had a different spelling of a particular GP. Which meant the remapping of the data had to be done manually looking at the map, the data, other sources, and sometimes guessing if this was the correct GP or not. This ended up being a manual process for every AC, as we didn’t do this mapping and standardization in the beginning.

While the delay caused problems with the maps being used in the election, they were worth doing to understand the problems with the data and the ground partner identified with the maps the most. By the end we were able to produced districts posters for the different parameters.

Sample report card

 

Final Posters

PC_sanitation copy poster madhubani_wateraccess copy poster madhubani_sourceprofileposters madhubani_sanitation poster Supaul_wateraccess copy poster copy Supaul_sourceprofile poster copy Supaul_sanitation copy poster copy Saharsa_wateraccess copy poster copy Saharsa_sourceprofile poster copy Saharsa_sanitation copy poster copy PC_wateraccess copy poster PC_sourceprofile poster

 

Lessons for next time

We learned a lot from this process. Mainly that the issues with standardization of Indian names in data is a real concern. While initiatives like Data.Gov.In are an important first step, it will take real will and dedication to work out this problem.

NGOs and groups that don’t work with data at the scale of modern data techniques are not always familiar with issues like formats, standardization problems, data interoperability,visualization and mapping to other datasets. This means that more time needs to be spent getting the intentions of the project out of the partner not just outputs. Problems like PDFs are not things everyone thinks about so the extra time of working with the partner to understand what data they want and find way to get it are better spent then converting PDFs to CSV if we don’t have to.

Designers are important, I created and designed the maps and posters, while I’m proud of them, they could have been done better and faster by a trained designer. Designers are worth the money and effort in order to make the final product really reflect the care and work we put into the data.

I consider this experience a success, despite the setbacks, we learned how to manage a team that was not full time and how important the initial work with the ground partners are to create realistic deliverables and timelines.

You can get all the data on DataMeet’s github page. 

Big thanks to the Gramener team – Santhosh, Pratap and Girish for dedicating their free time to this.

Five Years of DataMeet Discussions

We consider 26/01/2011 as DataMeet birthday. Thats the day we talked about starting DataMeet and hence it is the birthday. But the first email to the group was sent by S.Anand on 27/01/2011. Its been five years since that first email. I took this opportunity to scrape the email list to see how we are doing and what we talked about in last five years.

Growth

Activity

Members have started 1525 and have sent in total 4570 emails. But most important is how many participate.
infogram

Category Members
No Emails 855
1 Emails 184
2 Emails 75
3 Emails 43
More than 3 189

Discussions

Go have a look at full view of the traffic graph. Except for few peaks the group has been fairly consistent.

Starters

We have discussed about 1525 in last five years. Here is the list of top 20 starters.

author total topics started
Nisha Thompson 199
Thejesh GN 164
sumandro 71
Sridhar Gutam 64
srinivas kodali 36
Gautam John 30
Sajjad Anwar 28
Pranesh Prakash 27
bawaza…@gmail.com 27
Venkatraman.S. 23
satyaakam 22
S Anand 21
Balaji Subbaraman 20
Nikhil VJ 19
Justin Meyers 15
Sanky 15
Dilip Damle 14
Maya Indira Ganesh 13
Shree 13

First Responders

The first responders are important when someone posts a question. They are the first ones to respond to the questions. As you would have guessed the list is different from the starters list.

author number first response
Devdatta Tengshe 36
Gautam John 36
Nisha Thompson 57
srinivas kodali 28
Thejesh GN 27
Sajjad Anwar 21
satyaakam 20
Arun Ganesh 16
Avinash Celestine 15
Venkatraman.S. 15
Anand Chitipothu 14
sumandro 13
Dilip Damle 10
JohnsonC 10
S Anand 10
Gora Mohanty 9
Meera K 9
Sabarish Karunakar 9
Nikhil VJ 8

Part of many discussions

These are the members who have participated the most.

author total_emails_sent
Nisha Thompson 397
Thejesh GN 297
Gautam John 158
srinivas kodali 128
sumandro 109
Sajjad Anwar 93
Arun Ganesh 88
Dilip Damle 88
Devdatta Tengshe 85
satyaakam 83
Sridhar Gutam 81
Avinash Celestine 73
Justin Meyers 71
S Anand 68
Pranesh Prakash 67
Venkatraman.S. 64
Nikhil VJ 55
Raphael Susewind 55
Anand Chitipothu 51

Topics

We have discussed many many topics over years. But there are some popular topics. I have the list of topics by most replies.

Starter date/time topic
Karthik Shashidhar 2015-05-04 23:00:01 Shapefiles for "complete" India
megha 2014-04-10 14:10:21 MP/MLA Shapes
Srihari Srinivasan 2013-03-06 22:59:44 List of BMTC Bus stops
Nisha Thompson 2014-05-20 23:51:49 Logo Contest Voting!
S Anand 2016-02-01 18:31:38 PIN code geocoding
Siddarth Raman 2014-04-17 16:16:29 Parliamentary Constituency to Assembly Constituency to Ward linkages
Nisha 2013-04-15 09:44:21 April's Bangalore DataMeet
Gautam John 2012-04-14 09:49:50 I Change My City
Arun Ganesh 2011-03-14 11:23:25 Licensing crowdourced data projects
Sharad Lele 2015-11-27 19:59:49 Census of India seems to have maps of everything!

We also get quite a bit of traffic through search engines. So here is the list of top topics by views.

username date_time views topic
Karthik Shashidhar 2015-05-04 23:00:01 12324 Shapefiles for "complete" India
S Anand 2016-02-01 18:31:38 4783 PIN code geocoding
srinivas kodali 2013-07-01 12:49:33 2291 GeoJson data of Indian states
Aashish Gupta 2014-02-24 10:23:12 763 1981 and 1991 district-wise census data
Justin Meyers 2014-07-26 22:05:13 668 Updated Taluk Shapefile!!
indro ray 2013-08-13 10:21:18 651 MCD Delhi Admin Boundary GIS map
My profile photo 2012-08-30 17:41:45 615 Bangalore – BBMP ward boundaries – shape files available now
megha 2014-04-10 14:10:21 556 MP/MLA Shapes
Kavita Arora 2012-09-13 23:32:25 546 Ward Wise data for Bangalore – 2011 census?
Renaud Misslin 2014-12-03 09:45:16 426 Delhi ward shapefile for census 2011 data

At last customary wordcloud of topics.

wordcloud_subjects_arrow2

Of course all the scrapers and data is available on github. Go ahead make your own visualizations.