Category Archives: Data

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.

To Hack or Not to Hack….

Hackathons are a source of confusion and frustration for us. DataMeet actively does not do them unless there is a very specific outcome the community wants like freeing a whole dataset or introducing open data to a new audience. We feel that they cause burn out, are not productive, and in general don’t help create a healthy community of civic tech and open data enthusiasts.

That is not to say we feel others shouldn’t do them, they are very good opportunities to spark discussion and introduce new audiences to problems in the social sector. DataKind and RHOK and numerous others host hackathons or variations of them regularly to stir the pot, bring new people into civic tech and they can be successful starts to long term connections and experiments. A lot of people in the DataMeet community participate and enjoy hackathons.

However, with great data access comes great responsibility. We always want to make sure that even if no output is achieved when a dataset is opened at least no harm should be done.

Last October an open data hackathon, Urban Hack, run by Hacker Earth, NASSCOM, XEROX, IBM and World Resource Institute India wanted to bring out open data and spark innovation in the transport and crime space by making datasets from Bangalore Metropolitan Transport Corporation (BMTC) and the Bangalore City Police available to work with. A DataMeet member (Srinivas Kodali) was participating, he is a huge transport data enthusiast and wanted to take a look at what is being made available.

In the morning shortly after it started I received a call from him that there is a dataset that was made available that seems to be violating privacy and data security. We contacted the organizers and they took it down, later we realized it was quite a sensitive dataset and a few hundred people had already downloaded it. We were also distressed that they had not clarified ownership of data, license of data, and had linked to sources like Open Bangalore  without specifying licensing, which violated the license.

The organizers were quite noted and had been involved with hackathons before so it was a little distressing to see these mistakes being made. We were concerned that the government partners (who had not participated in these types of events before) were also being exposed to poor practices. As smart cities initiatives take over the Indian urban space, we began to realize that this is a mistake that shouldn’t happen again.

Along with Centre for Internet and Society and Random Hacks of Kindness we sent the organizers, Bangalore City Police and BMTC a letter about the breach in protocol. We wanted to make sure everyone was aware of the issues and that measures were taken to not repeat these mistakes.

You can see the letter here:

We are very proud of the DataMeet community and Srinivas for bringing this violation to the attention of the organizers. As people who participate in hackathons and other data events it is imperative that privacy and security are kept in mind at all times. In a space like India where a lot of these concepts are new to institutions, like the Government, it is essential that we are always using opportunities not only to showcase the power of open data but also good practices for protecting privacy and ensuring security.

Guest Post: Varun Goel- Releasing Data for Agriculture

RRAN_logoVarun serves as the chief data scientist at a research team led by Dr. Ashwini Chhatre, serves as the Research Node of the Revitalizing Rainfed Agricultural Network – an India wide network of NGOs, civil society organizations, researchers, policy makers and think-tanks that aim to reconfigure the nature, amount and delivery of public investments for productive and resilient rainfed agriculture. 

The Combined Finance and Revenue Accounts (CFRA) report is an annual report prepared by the office  of the Comptroller and Auditor General (CAG) of India to provides comprehensive Union and State government data on audited receipts, revenue expenditures and capital outlay for different major, minor and sub-minor heads.

Since the figures for actual expenditures on different heads may differ from actual  budget allocation by as much 15 to 20 percent, and that each state might have different procedures of auditing, the CFRA data provides reliable and fairly disaggregated figures of public expenditure, audited by a central authority.

The research team at the Revitalizing Rainfed Agricultural Network (RRAN) has scraped and processed the CFRA data from 2005-06 to 2010-11 for all general and economic services to understand statewide public investments in agriculture and allied activities, and highlight the mismatch in investment and needs on the ground.

The processed data, along with detailed information for each head can be forked here.

Although the data is only available at the state level, it can provide valuable insight on not just public expenditure in other domains such as urban development, health, central and state sponsored schemes, but also highlight the differences in budget allocation and actual spending of various government heads.

Revitalizing Rainfed Agricultural Network (RRAN) has practice and policy node that generates ground based evidence and block, district and state level for policy engagement, the research node’s objective is to generate evidence for testing key hypotheses to enable an articulation of the nature and magnitude of public support needed to fuel growth of India’s rainfed agriculture. To facilitate this, a Data Center has been set up with the aim of acquiring, reconciling, processing, visualizing and disseminating pan India datasets to assist in exploratory analysis and develop research hypothesis, backing up policy advocacy through scientifically rigorous data analysis, and implementing data-driven decision-making tools for program implementation by grass-roots level organizations.

Open Access Week – Open a Dataset with Srinivas Kodali

Cross post from Lost Programmer

Starting today it is International Open Access Week, I have been associated with concepts of open data and open access since 2012 and was hoping to bring some serious attention to it in India. This week I intend to showcase a serious of datasets which several departments of Govt. of India publishes in there web portals through NDSAP apart from Open Government Data Platform

Today’s dataset which I want to bring attention is of Indian Customs. Indian customs maintains records of every product imported and exported through land, sea and air. They publish this data through their commerce portal. They should be highly appreciated for maintaining this website and publishing the data. The data is published as per Notification No. 18/2012-Customs (N.T) dated: 5th Mar, 2012

The data being published includes origin, destination ports, name of the product, Harmonized System code of the product, quantity of product, unit quantity of the product, customs valuation of the product. For imported goods, the origin country is published instead of the port, while for export you get to know the exact destination city.

Read the rest over at Srinivas’s blog here

And if you are using the data for anything please let us know! Stay tuned for tomorrow’s release!

Open Access Week 2015 India Events

It’s Open Access Week! This week there are events around the country to celebrate openness and explore how far we have to go.

MapBox is putting up an amazing Open Data Gallery Tuesday the 20th in Bangalore. Come and hangout look at incredible art and projects from around the country!

In celebration DataMeet is doing its first MULTI CITY EVENT!

Join us Saturday 24th at 6:30pm for talks from Data.Gov.In, Ahmedabad and Bangalore with livestreaming between the cities!

  • Data.Gov.In will talk about the latest updates to Open Data in India.
  • Bangalore will discuss open access in general and open data projects.
  • Ahmedabad will talk about the status of Open Access in their part of the world.
  • Srinivas Kodali will talk about releasing datasets.

Bangalore’s event will be at Centre for Internet and Society.

Ahmedabad will be at CEPT University. 

Please RSVP on Facebook or Meetup.

Let’s celebrate all we have been able to accomplish as a community and look forward to continuing to promote a culture of openness, sharing, learning and collaboration.

 

{Ahmedabad} – 2nd Meetup

Data{Meet} Ahmedabad – 2nd Meetup

Data{Meet} Ahmedabad - 2nd Meeting

Data{Meet} Ahmedabad – 2nd Meeting

Our 2nd meetup was held at IIM-A, under the aegis of the RTE Resource Centre, with 20 participants; half of them had attended the 1st meetup.

Talk #1: All walls come down – by Ashish Ranjan, RTE Resource Centre, IIM-A

The first talk in the 2nd DataMeet of Ahmedabad Chapter brought forward the efforts being put together by the team RTE, working out of IIM-Ahmedabad. The team members present at the venue were Prof. Ankur Sarin, Ashish Ranjan, Advaita R and Nishank Varshney. Ashish presented their journey of supporting the implementation of RTE in the state of Gujarat.

The Right to Education (RTE) act Section 12 requires schools to enrol a certain number of children from economically weaker families. The RTE Resource Centre (rterc.in) organises pre-enrolment campaigns for the benefit of prospective students and their parents, and has enlisted NGOs for hand-holding the children post-enrolment. The talk gave a glimpse of their experience in Ahmedabad, observations from Maharashtra, and the data-related challenges they faced.

datameet2

The management of this important activity was being done manually. This threw up many problems:
The registration of beneficiary families was often incomplete, with partial addresses – recording just the area of residence e.g. “Jamalpur”. This lead to many parents complaining about non-receipt of allotment letters..
There was no mapping of schools or beneficiary families, which could have aided better matching of children and schools.

A study by the team RTE revealed how a large number of schools were finding their way around the RTE mandate. These methods include making demands of un-required documentation of the parents to, tricking the MIS systems which enable applications from parents, into counting the ages of the children eligible for the schools as ineligible amongst various others. Nishank pitched in with instances from Maharashtra, where the minimum and maximum permissible age limits were deliberately entered by schools in such a way that potential students would be under age during an admission year, and over age the next year, effectively excluding them. In some particularly bad cases, the difference was one day: the child would have to be born on a specific date. For lack of efficient and transparent allotment processes, there were cases of candidates getting multiple admissions (as much as 18) while some did not get any. To bring out all these analyses, though, the school and student data from the Maharashtra RTE website had to be painstakingly downloaded, manually. Many DMers offered support to gather this data more easily.

datameet3

The team was quite inspired by the school map of the Karnataka Learning Partnership (klp.org.in/map) and wants to build such a comprehensive tool for themselves, with features to find schools within a specified distance, and help match students with schools. Unlike the Karnataka programme, there still is no MIS in place to facilitate the enrolment and selection process. Shravan suggested that it might be possible to use the codebase of KLP and adapt it for use in Ahmedabad. Hopefully, the D{M} folks will volunteer for the necessary support.

The RTE team also wants to build a tool to track the performance of enrolled students. They discussed about the potential privacy issues involved in this. It was suggested that the performance reporting to be published on the website could be at an appropriate level of aggregation which safeguards privacy and preserves discernible performance stats. The possibility of using ODK for volunteer led data collection was also discussed.
Getting together at the meetup opened up many possibilities for collaboration from the participants as a few of them came forward with suggestions and also extended their support to this cause.

Talk #2: Public Transport of Ahmedabad

Jayesh Gohel is not your everyday architect. He dropped out of his course at CEPT because he got too interested in code and soon enough he started enjoying making websites. Being an Amdavadi, he noticed the lack of infrastructure, both digital and non-digital in supporting the commuting that AMTS enabled in the city and so he decided to work on amtsinfo.in – the unofficial official support and information website for the Ahmedabad Municipal Transport Service.

Jaye

At the 2nd Datameet in Ahmedabad, Jayesh inspired the audience with his experiences with developing the website with the sole aim of solving the information problem related to the rather important and convenient network that AMTS is. Jayesh’s talk was simple and spoke about his personal motivations and learnings in the course of the development of this app. It also brought to the light the issues that plague the archaic systems that govern our modern lives, which can otherwise be so easily solved with the use of digital technology. However, ‘there’s hope if all of us take initiatives’, Jayesh said.

Nepal Needs You to Make Maps!

Post by Tejas AP

The Humanitarian OpenStreetMap Team (HOTOSM) has activated to support crisis response in Nepal after the recent devastating earthquake. A global team of volunteers is contributing to the OSM project by mapping physical infrastructure (roads and buildings) as well as traces and areas safe for crisis responders to use and congregate at. We believe improved information, especially of the remote affected areas, is crucial to improve the efforts carried out by relief agencies on-ground.

Volunteers may contribute to the map of Nepal simply by selecting a task from the wiki. Basic questions about registering and using the OSM mapping tool can be found in its comprehensive documentation here.

While the volunteers have been recording road networks and buildings at a rapid pace, we understand that the communication networks in Nepal are still being restored, and crisis responders might not have access to navigation maps to expedite their efforts. We want to help in ensuring that people have access to map data in every manner possible.

We want to print offline maps and send them with relief materials from India to Nepal. Please help us by providing us

* a list of towns/villages/regions you need maps of, and

* point-of-contact we can deliver the printed maps to.

For more information, please get in touch with

Sajjad  – sajjad(at)mapbox(dot)com

Tejas  – tejaspande(at)live(dot)com

Nisha  – nisha(at)datameet(dot)com

Prabhas – prabhas.pokharel(at)gmail(dot)com

Meanwhile, here’s some information that you might find useful  –

1. https://www.mapbox.com/blog/nepal-earthquake/
2. The News Minute report –
http://www.thenewsminute.com/article/how-group-individuals-all-over-india-are-making-maps-help-rescue-teams-nepal
3. HOT Wiki – https://wiki.openstreetmap.org/wiki/2015_Nepal_earthquake
4. KLL report – http://kathmandulivinglabs.org/blog/

Open Transit Data for India

(Suvajit is a member of DataMeet’s Transportation working group, along with Srinivas Kodali, we are working on how to make more transit related data available.)

Mobility is one of the fundamental needs of humanity. And mobility with a shared mode of transport is undoubtedly the best from all quarters – socially, economically & environmentally. The key to effective shared mode of transport (termed as Public Transport) is “Information”. In India cities, lack of information has been cited as the primary reason for deterrence of Public Transport.

Transport Agencies are commissioning Intelligent Transport Systems (ITS) in various mode and capacity to make their system better and to meet the new transport challenges. Vehicle Tracking System, Electronic Ticketing Machines, Planning & Scheduling software are all engines of data creation. On the other side, advent of smart mobile devices in everyone’s hand is bringing in new opportunities to make people much more information reliant.

But the demand for transit data is remarkably low. The transit user and even transit data users like City Planners should demand for it.
The demand for Public Transport data in India should be for the following aspects:

A. Availability
To make operation and infrastructure data of Transport operators easily available as information to passengers in well defined order to plan their trip using available modes of Public Transport.

B. Interoperability
To make transit data provided by multiple agencies for different modes (bus, metro, rail) usable and make multi modal trip planning possible.

C. Usability
To publish transit oriented data in standard exchange format across agencies in regular frequencies to provide comprehensive, accurate and updated data for study, research, analysis, planning and system development.

D. Standardisation
To be a part of Passenger charter of Transport Operators to publish their data in standard format and frequency. This can also serve as a guideline for Transporter Operator while commissioning any system like Vehicle Tracking System, ITS, Passenger Information System, website etc.

What kind of Transit data is needed ?

  • Service Planning data

It will comprise of data on bus stops, stations, routes, geographic alignment, timetables, fare charts. With this dataset, general information on transit service can be easily gathered to plan a journey. Trip Planning mobile apps, portals etc can consume this data to provide ready and usable information for commuters.

  • Real time data

A commuter is driven by lot of anxieties when they depend on public transport mode. Some common queries; “When will the bus arrive ?”, “Where is my bus now?”, “Will I get a seat in the bus ?”, “Hope the bus has not deviated and not taking my bus stop.”.

Answer to all this queries can be attended via real time data like Estimated Time of Arrival (ETA), Position of the vehicle, Occupancy level , Alert and Diversion messages etc. Transport Operator equipped with Tracking systems should be able to provide these data.

  • Operational & Statistical Data

A Transport Operators operational data comprises of ticket sales, data of operation infrastructure and resources like Depots, Buses, Crew, Workshops etc. As operatore are tending towards digital mode of managing these data it also makes a good option to publish them at regular intervals.

A general commuter might not be interested in this data, but it will very useful for City Planners to analyse the trend of commute in the city and make informed decision. City transport infrastructure can be planned to orient it towards transit needs and demands.

The transport agency can benefit highly by demonstrating accountability and transparency. They can uplift their image as a committed service provider thereby gaining for passengers for their service.

So, together it will make a thriving landscape, if the data creators of Public Transport in India provide their data in Open which can be consumed by a larger set of people to build platforms, applications, solutions for transport study, analysis & planning across different section of users.

Open Transit Data is the tipping point for Smart Mobility in India.

That is why we have started putting our thoughts together and began writing an Open Transport Data Mainfesto.

Data On the Ground: Crosspost from India Water Portal

From India Water Portal.  Communities using planning, data and collaboration to take control of their water security.  

Excerpt

What stands out in Dholavira, is the attention to detail when it came to collecting and storing water. More than 16 reservoirs are said to exist on this 100 hectare site, of which 5 have been excavated. While these reservoirs harvested rainwater, an elaborate system of drainage channels was planned to ensure that all the runoff collected in these tanks. “See this reservoir,” Raujibhai says, “it has a well inside it so that even if the tank dries up, the well will supply water”. There is also a standalone well and a seasonal stream, which was dammed at multiple points to harvest water.

Their water mantra was simple: collect and store water locally and conserve it to provide fresh water. This continues to be relevant even today for the 1.7 lakh people who live in Rapar, the taluka where Dholavira is located.

Rainfall map of India. Historically, Rapar has received poor rainfall. (Source: IWP)

Rainfall map of India. Historically, Rapar has received poor rainfall. (Source: IWP)

This has been proven by Samerth, an organisation that has worked with communities in 20 Gram Panchayats in Rapar to create structures that can store 64 million cubic feet of water. What are the elements that are common? How is Rapar’s water security now?

 

Read the rest of this amazing story here.

Rebuilding the Karnataka Learning Partnership Platform

The Karnataka Learning Partnership recently launched a new version of their platform. This post talks about why they are building this and also some of the features and details. This is cross-posted from their blog.

Over the past five months we have been busy rearchitecting our infrastructure at Karnataka Learning Partnership. Today, we are launching the beta version of the website and the API that powers most of it. There are still a few rough edges and incomplete features, but we think it is important to release early and get your feedback. We wanted to write this blog post along with the release to give you an overview of what has changed and some of the details of why we think this is a better way of doing it.

Data

We have a semi-federated database architecture. There is data from Akshara, Akshaya Patra, DISE and other partners; geographic data, aggregations and meta-data to help make sense of a lot of this. From our experience PostgreSQL is perhaps the most versatile open-source database management system out there, Especially when we have large amounts of geographic data. As part of this rewrite, we upgraded to PostgreSQL 9.3, which means better performance and new features.

Writing a web application which reads from multiple databases can be a difficult task. The trick is make sure that there is the right amount of cohesiveness. We are using Materialized Views in PostgreSQL. Materialized View is a database object that stores the result of a query in a on-disk table structure. They can be indexed separately and offer higher performance and flexibility compared to ordinary database views. We bring the data in multiple databases together using Materialized Views and refreshing them periodically.

We have a few new datasets – MP/MLA geographic boundaries, PIN code boundaries and aggregations of various parameters for schools.

API

The majority of efforts during the rewrite went into making the API, user interface and experience. We started by writing down some background. The exhaustive list of things that the API can do are here.

We have a fairly strong Python background and it has proven to be sustainable at many levels. Considering the skill-sets of our team and our preference for readable, maintainable code, Django was an obvious choice as our back-end framework. Django is a popular web development framework for Python.

Since we were building a fairly extensive API including user authentication, etc., we quickly realized that it would be useful to use one of the many API frameworks built on top of Django. After some experimentation with a few different frameworks, we settled on using Django-Rest-Framework. Our aim was to build on a clean, RESTful API design, and the paradigms offered by Rest-Framework suited that perfectly. There was a bit of a learning curve to get used to concepts like Serializers, API Views, etc. that Rest-Framework provides, but we feel it has allowed us to accomplish a lot of complex behaviours while maintaining a clean, modular, readable code-base.

Design

For our front-end, we were working with the awesome folks at Uncommon, who provided us gorgeous templates to work with. After lengthy discussions and evaluating various front-end frameworks, we felt none of them quite suited what we were doing, and involved too much overhead. Most front-end frameworks are geared toward making Single Page Apps and while each of our individual pages have a fair amount of complexity, we did not want to convert everything into a giant single page app, as our experience has shown that can quickly lead to spiraling complexity, regardless of the frame-work one uses.

We decided to keep things simple and use basic modular Javascript concepts and techniques to provide a wrapper around the templates that Uncommon had provided and talk to our API to get and post data. This worked out pretty well, allowing us to keep various modules separated, re-use code provided by the design team as much as possible, and not have to spend additional hours and days fighting to fit our code into the conventions of a framework.
All code, design and architecture decisions are in the open, much like how rest of our organisation works. You can see the code and the activity log in our Github account.

Features

For the most part, this beta release attempts to duplicate what we had in v10.0 of the KLP website. However, there are a few new features and few features that have not yet made it through and a number of features and improvements due in future revisions.

Aside from the API, there are a few important new features worth exploring:

  1. The compare feature available at the school and pre-school level. This allows you to compare any two schools or pre-schools.

    1. Planned Improvements: The ability to compare at all and any levels of hierarchy; a block to a block or even a block to a district etc.

  2. The volunteer feature allows partner organisations to post volunteer opportunities and events at schools and pre-schools. It also allows users to sign up for such events.

    1. Planned Improvements: Richer volunteer and organisation profiles and social sharing options.

  3. The search box on the map now searches through school names, hierarchy (district, block etc.) names, elected representative constituency names and PIN Codes.

    1. Planned Improvements: To add neighbourhood and name based location search.

  4. An all new map page powered by our own tile server.

  5. Our raw data page is now powered by APIs and the data is always current unlike our previous version which had static CSV files.

    1. Planned Improvements: To add timestamps to the files and to provide more data sources for download.

Now that we have a fairly stable new code base for the KLP website, there are a few features from the old site that we still need to add:

  1. Assessment data and visualisations of class, school and hierarchy performance in learning assessments needs to be added. The reason we have chosen not to add it just yet is because we are modifying our assessment analysis and visualisation methodology to be simpler to understand.

  2. Detail pages for higher levels of aggregation – like a cluster, block and district with information aggregated to that level.

  3. A refresh of the KLP database to bring it up to date with the current academic year. All these three have not been done for the same reason; because this requires an exhaustive refactor of the existing database to support the new assessment schemas and aggregation and comparison logic.

 

Aside from the three above, we have a few more features that have been designed and written but did not make it in to the current release.

  1. Like the volunteer workflow, we have a donation workflow that allows partner organisations to post donation requirements on behalf of the schools and pre-schools they work with for things these schools and pre-schools require and other in-kind donations. For example, a school might want to set up a computer lab and requires a number of individual items to make it happen. Users can choose to donate either the entire lab or individual items and the partner organisation will help deal with the logistics of the donation.

 

Our next release is due mid-October to include the volunteer work flow and squish bugs. Post that, we will have a major release in mid-January with the refactored databases and all of the changes that it enables and all the planned improvements listed above. And yes, we do have a mobile application on our minds too.

The DISE application will be updated with the current years data as well by November. We will also add the ability to be able to compare any two schools or hierarchies by December.

So that’s where we are, four years on. The KLP model continues to grow and we now believe we have a robust base on which to rapidly build upon and deploy continuously.

For the record, this is version 11. 🙂