Set theory, Systems and Jaga Mission

In his classic book 'A System's View of Planning', George Chadwick wrote:

"Not only can the whole of mathematics be developed from the concept of a set, but, as foreshadowed, the idea of a system stems naturally from that of a set." (p-28)

While we all studied set theory in high school mathematics, its usefulness in making sense of the structure and behaviour of complex systems encountered regularly in urban planning, was never discussed adequately in planning school. The consequence is the absence of yet another powerful tool from the contemporary planners' toolkit and the state of confoundedness that naturally follows.

Created by the German mathematician Georg Cantor in 1874, set theory "stems from the simple idea of a number of things which have a common property or properties and thus can be represented as elements of a set." (ibid)

The relationship of set theory with the systems view of planning is made amply clear when we consider that, "the commonly accepted definition of a system is a set of entities and the relationships between them."

Regions and Sets

Let us consider how set theory helps us to tackle the complexity in Jaga Mission, the flagship slum land-titling and upgrading project of the Government of Odisha, India. But before plunging into that, let's have a quick look at how set theory came to be an integral part of regional science already by the 1960s. 

In his classic paper, 'Mathematical Aspects of the Formalization of Regional Geographic Characteristics' , the Soviet geographer B.B. Rodoman wrote that, if a region is viewed as a set of subregions, then one could "convert into the language of geography the theorem of the five alternative relationships which is part of set theory." 

He elaborated further that, according to set theory, two regions A and B may have the following relationships with each other:

1) They may have no common territory

2) They may intersect

3) A may be part of B

4) B may be part of A

5) They may be identical

The relationships can be expressed as follows by using the symbols of set theory:

1) A ∩ B = ∅    [intersection of A and B is a null set]

2) A ∩ B ≠ ∅ ; A ∩ B ≠ A ; A ∩ B ≠ B   [intersection of A and B is not a null set]

3) A ∩ B ≠ ∅ ; A ∩ B = A ; A ∩ B ≠ B ; A ⊂ B   [A is a sub-set of B]

4) A ∩ B ≠ ∅ ; A ∩ B ≠ A ; A ∩ B = B ; B ⊂ A    [B is a sub-set of A]   

5) A ∩ B ≠ ∅ ; A ∩ B = A ; A ∩ B = B ; A = B    [A is equal to B]

By adding to the above the relationships of the sets with their complements (i.e. the elements present in the universal set but not in the set itself - basically the world outside of itself), one can show the full range of ways in which various overlapping or separated regions interact with each other. This was explained very clearly through an example of wheat growing regions, vegetable growing regions and corn growing regions in Golledge and Amadeo's paper titled 'Some introductory notes on regional division and set theory'

It is clear from the diagram above that every part of the three fields, no matter how complex, could be accurately described using the language of sets. For example parts 4 and 6, which occupy the central part of the fields, where all three type of fields intersect can be described using the following notations -

For part 4 --> (W ∩ V) ∪ (W ∩ C)        

[i.e. the union of the intersection of wheat and vegetable and the intersection of wheat and corn]

For part 5 --> (C ∩ W) ∪ (C ∩ V)

[i.e. the union of the intersection of corn and wheat and the intersection of corn and vegetable]

It is easy to spot the origins of the various vector operations in GIS using logical operations such as AND, OR, != (corresponding to intersection, union and not equal to) etc from the above discussion on set theory and regionalization.  

Slums and Sets 

Any slum land titling project is complex by its very nature, but Jaga Mission is quite the Godzilla of complexity due to its size and geographical coverage. Unlike, slum titling and upgrading projects that target a couple of major cities, the Mission covers all 2919 slums in all 115 cities and towns in the state.

However, by combining the necessary geo-spatial datasets corresponding to the various operational parameters of the mission one can readily apply set theory to simplify and automate the tasks. This was particularly true in the case of the trickiest component of any land titling project - the land parcels themselves. 

In fact, one is bound to spot the visual similarity in the following image of a slum of Jaga Mission shown below and the illustrative diagram of the three fields in Golledge and Amadeo's paper.

The above map shows the location of slum houses overlaid on land parcels which belong to three types - Leasable government land (on which slum land rights can be granted); Reserved government land (on which slum land rights can be granted only after a category conversion process); and Private land (on which slum land rights cannot be granted).

If A is the set of slum houses and B is the set of government leasable land parcels then the slum houses entitled to land titles straight away would be given by - 

A ∩ B    

However, if one would consider the total set of slum houses which are entitled to land titles once the land category conversion for reserved government land parcels are completed (reserved parcels given by set C), then that would be given by -

A ∩ (B ∪ C)

If private land parcels are the only category over which land titles cannot be granted (set D) then the set of entitled slum houses could also be given by -

A ∩ D'    [where D' is the complement of set D]

By defining the sets according to the specific parameters of the mission, the outcome of the interaction of various parameters could be computed by applying the theorem of alternate relationships.

Once such relationships are established then it really does not matter if the process needs to be done for one slum or for a 100 slum or for a 1000 slums. Nor is it any difficulty to divide a particular set into its constituent sub-sets (for example the reserved government land category itself is a union of numerous subsets of land parcel types distinguished by the land-use type and the ownership type -- these particulars can also be described as sets of their own).

Planning and the Complexity Conundrum

Implementing at the cost of Planning

A characteristic feature of large urban development schemes in India is that they are heavily implementation oriented. In a way, they seem to have overcome the "the-plan-was-good-but-the-implementation-was-bad" impasse in Indian urban planning.

To be sure, this lacuna of planning has been overcomeby abandoning planning itself and opting for large, sectoral schemes implemented by specific line departments of the government.  For what else are these large urban development schemes in India if not projects of specific infrastructure verticals undertaken in mega-scale?

It is not difficult to resolve the difficulties of planning, if urban planning itself -- the science and art of forecasting future development scenarios in a city or region and preparing for it by integrating the activities of multiple components of the urban system i.e. land, housing, economy, infrastructure -- is abandoned.

Urban systems are complex by their very nature. The discipline of urban planning, therefore, by its very nature, is tasked with anticipating the behaviour of this highly complex and dynamic system in a scientific manner and preparing for it. 

Planning and Complexity

Rather than talking in vague and general terms (as is increasingly common these days), it is perhaps better to use the concept of "variety", which is used in the field of cybernetics as a measure of complexity.

W. Ross Ashby, one of the pioneers in the field of cybernetics, described "variety" as the number of possible states that a system can take. As systems become larger in size, the amount of "variety", and therefore complexity, increases exponentially.

Looking at urban systems, examples of this could be found everywhere. For a very small town a single, small commercial area with a small cluster of shops of various kinds may suffice. But a larger city would inevitable give rise to a whole range of commercial areas of various sizes and types, having different areas of coverage and located in various parts of the city.

I sometimes gave the example of a footpath to my planning students, which, in a sweet little Scandinavian town would be just a footpath, but in a city of even moderate size in India could turn into a space for shopping, hawking, begging, sleeping, living, storing, parking...and, if possible, a space for pedestrians for walking. 

In the former case the footpath would have a variety of 1 and in the latter a variety of n...and counting !

In his book "Designing Freedom", Stafford Beer showed the calculation to estimate variety -

If there are n people in a system, and each of them has variety x (each can adopt x number of possible states), then the variety of the total system thus defined will be xⁿ.

So if there are only 40 people (n=40), each of whom has only two possible states (x=2), there are still 2⁴⁰ possible states of the system.

2⁴⁰ =  1,099,511,627,776

      ('Designing Freedom', Stafford Beer, p - 11)                 

 

This is complexity quantified.

Better to not even attempt to calculate the total possible states that our second footpath can take ! 

Now that we have developed a healthy respect for the mind-numbing salvo of variety (therefore complexity) that urban systems can hurl at the planning profession, it is perhaps possible to at least understand (if not entirely forgive) why the profession often fails to successfully execute the task that it has taken on.

The planning paradox and the world of probabilities

Any serious discussion on a topic as complex as the planning of urban systems in the 21st century, has to begin by acknowledging that it is a near impossible task. And yet, it has to be done.

Therefore, the discipline has to be approached like any complex system has to be approached - not with the demands of certainty...but with the estimation of probability.

As the geopolitical expert Andrei Martyanov said in a recent talk -

"The world of prognostication and serious analysis is the world of probabilities."

In the context of planning, this was discussed wonderfully by Poulicos Prastacos in one of his papers on the Projective Optimization Land Use Information System (POLIS) land-use transportation model. He wrote that one of the problems of the first generation of land use-transportation models, developed by planners in the US during 1960-75, was that their goals were too ambitious. 

Prastacos wrote the following lines way back in 1985, which I find extremely relevant given the planning challenges we face today -

"Critics of urban modelling were correct in pin-pointing the limitations of the early models, but failed to notice that most of these arose from either the overambitious expectations about the role of models in planning or the general lack of knowledge about the state of the art and the capability to implement successfully complex mathematical equations. They did not provide an alternative methodology that could address some of the more modest goals and potential applications of large-scale models (consistent set of forecasts, evaluation of alternative transportation improvements)."

Instead of an abandonment of the models, the empirical criticism should have instead allowed for the calibration of the goals and ambitions - applying them to more modest problems and building them up based on the results.  

If 'certainty' is impossible, then it is pointless to keep it as the only measure of success. There is no methodology that exists which can forecast the future population of a city with certainty. However, there are many splendid methods by which the future population can be estimated, depending on the assumptions used. 

Abandoning probability based scientific methods (just because they "fail" to offer the certainty demanded by decision-makers) relegates planning to a position where its only hopes are various kinds of purely qualitative discursive practices; the tribal knowledge of long established planning offices and the individual genius of this or that planning officer, engineer or administrator etc.

That's no way to handle a system, let alone a complex and dynamic one such as the urban system. 

This dooms the profession to be eternally engaged with last minute fire-fighting with ad hocism as its primary tool.

Compared to this visible form of acting and responding to various urban challenges, the voluminous master plans (where they exist) with all their guidelines, development controls,  land-use plans seem unrealistic and farcical like the detailed diet-chart of a person who is gobbling junk food everyday because he never gets enough time to cook and eat healthy food.

This is also what strengthen the arguments in favour of ditching planning altogether, or effectively bypassing it by directly implementing the separate infrastructure components without requiring an overall plan to guide the process. 

Thus a mission like the Atal Mission for Rejuvenation and Transformation  (AMRUT) would cover a range of infrastructure veticals such as water supply, sewerage and septage management, storm water drainage to reduce flooding, green spaces and parks and non-motorised transport. Similarly, Swachh Bharat Mission would focus on construction of toilets, solid waste management etc. 

As the implementation of these verticals can be measured in the form of easily quantifiable metrics - number of toilets constructed; kilometers of drains laid; number of parks made etc - they, naturally, become favourites of politicians and bureaucrats alike.

But this can lead to serious problems.

Duplication dilemma

A distinct benefit of planning, even when it totally fails to predict or influence the course of urban development, is its ability to get some sense - however limited - of how the different components of the urban system interact with each other. Being obliged to operate over a specific geographical territory it can at least figure out how the various sectoral components are located with respect to each other. Operating purely within the sectoral domains eliminates this advantage. In fact, this is not very different from the arguments offered in favour of economic planning - the ability to monitor the activities of individual firms and attempt to coordinate them for the fulfillment of overall plan targets - as opposed to a purely market driven approach where each firm strives to maximise its profits irrespective of the consequences that may have for the overall economy and the environment.  

The eagerness to maximise the implementation of individual sectoral schemes leads to a tendency to overlook how different sectors interact with each other in an urban system.

Just take a look at the image below. It shows the location of a slum in the northern part of the city of Bhubaneswar. Right next to it, one can see the affordable housing units being constructed to house the residents of the slum shown in the image and also the residents of other neighbouring slums. 

The affordable housing units were being constructed under a public private partnership model and overseen by the Bhubaneswar Development Authority and the Bhubaneswar Municipal Corporation (a process which had been in the works almost since 2017). In the meantime, Jaga Mission - the flagship slum land-titling and upgrading programme of the Government of Odisha - was launched and was overseen by the Department of Housing and Urban Development.

Jaga Mission selected the same slum in its pilot phase of slum upgrading in 2020-2021 and did a very fine job of upgrading it in consultation with the residents of the slum.

However, a successful of upgrading of this slum means that there really is no need for these families to move into the nearby affordable housing site. Not only would the families have no need of moving there, the units of the housing site may now need to be filled by families of slums which are located further away - hence increasing the probability of reluctance of the residents of even those slums to move in here. 

Each scheme aimed at maximising their individual benefits, without considering that they may just end up duplicating the benefits churned out by another scheme.

A more planned and coordinated approach would probably have been to choose other slums for the pilot upgrading phase and let the present slum be catered to by the affordable units - since they were already under construction. 

This is just one example. In a situation where such lack of coordination and a continuous maximisation of implementation of individual sector verticals is the norm, such examples are innumerable and constantly proliferating. 

Yet, there are ways to turn this situation around by making intelligent use of the tools and techniques that are available to us - that we either tend to forget or abandon.

As Prastacos pointed out - we don't need to throw away our tools...we may only need to make the goals more modest and realistic.

More on that in future blogs...

 

 

(Smart when you create...dumb when you consume) <-- How to recognise unnecessary technology and some Jaga Mission Stories

The challenge of humanity, since the industrial revolution, has not been one of scarcity, but one of excess (and of the exploitation and inequality that naturally appear when that excess - which can provide for the whole world - ends up being controlled by the few). 

The trouble with technology is the same. How much is enough ? Where does one draw the line between the need and the want...the useful and the useless ?

Perhaps, there is a simple way to distinguish the technology that one needs from the technology that is unnecessary, superfluous and, in all probability, harmful.

Any technology which makes you smarter while you use it and keeps making you smarter and more creative the more you use it, is a healthy and useful technology for you. The technology that makes you dumb and dependent when you use it, is neither very useful nor very healthy in the long run, irrespective of the convenience that it brings.

Most of the time, we use (rather consume) technology that makes the creator of that technology - and those who control the creators - smarter and more powerful, while making us dependent and constantly distracted (which cannot but cause a steady dumbing down over time).

Technology ...Consumer and Creator

A casual glance at the way people use their computers - one of the most powerful tools of modern technology - can confirm the above statements.

Google-maps may make map-reading, navigation and orientation very easy, but it can also decrease our ability to use our sense of direction, powers of observation and of memory to remember and locate landmarks, judge distances etc.

Typically, when we use google-maps our eyes stay glued to the smart-phone screen (the effect is the same even if we are looking at the road while driving...the app tells us everything), but if we were to go old-style with paper maps we would have to constantly look up from the map to scan the surroundings and ensure that we are at the right spot.

Of course, I am absolutely not suggesting dumping google-maps and returning to paper maps. After all the whole purpose of technology is to make life more convenient for humans so that they don’t have to engage in ceaseless manual labour and mundane tasks and engage in more meaningful pursuits instead.

But if the consequence of such “convenience” is a steady process of dumbing down and engaging in such "meaningful pursuits" like spending hours on social media and the "struggles" of becoming an influencer, then it might actually be healthier and more meaningful to return to a life of heavy manual labour (if one can, that is).

The idea is not to turn ones back on technology (it simply cannot be done), but to be aware of the manner in which the technology is owned, controlled and provided to us so that we can be conscious of its effects on us.

GPS and the Kargil Lesson

Talking about the conveniences of positioning systems, that is exactly the lesson that the Indian army learnt the hard way during the Kargil war of 1999. India was denied access to the Global Positioning System (GPS) by USA exactly when she needed it most in the context of high-altitude mountain warfare. The Kargil war was also a trigger event that led to the development of NavIC (’Navigation with Indian Constellation’...the word ‘navik’ means sailor in Hindi), India’s alternative to the GPS. 

Therefore, the simple learning that the above case provides, is that it is alright to be a user of technology, as long as you also play a role in developing it (or at least understanding how it operates), but it can be downright dangerous if you forever remain merely a consumer of technology.

Still, no matter how dependent google-map may make you, it is still a very useful tool. What arguments could one possibly offer to justify the helpless addictions that are caused by the largely useless social-media platforms ?

I am sure the people who develop these platforms continue to sharpen and develop their skills in programming and problem solving, whereas the users continuously lose the ability to use the computer for the main task it was created to perform – to compute. On top of that, the more they use...the more they generate data for these very same companies.

One cannot wait for society to change to protect oneself from such devastating trends...one simply has to jump off this crazy train oneself.

Linux and Synaptic Connections

For me that jump was in April 2016, when I made the switch from Windows to Linux...and never looked back.

I realised that the users of open-source operating systems and software, inevitably, start transforming into developers with time.

Or as my dear friend Titusz Bugya, who introduced me to Linux and taught me pretty much everything that I know about the proper use of computers, once put it jokingly-

"Linux IS user-friendly !! It is a friend of the User...not of the idiot !"

As the Linux beginner starts overcoming the hesitation and fear of the terminal window and has the first conversations with the computer using the command line, s/he begins to hone that most essential and fundamental skill required for solving a problem, no matter how complex -- the ability to formulate a question.

The clearly formulated question leads to the precisely formulated command and that leads to the desired result.

Here, the Unix philosophy, which is also used in Linux, of using programs that do only one thing and do it very well becomes a great tool. It encourages you to break down complex tasks into component parts - which by themselves may not be as overwhelming - and then deploy the appropriate programs to tackle them one at a time. 

It is not just about approaching and successfully completing a task, but about developing a certain way of thinking and approaching a problem - or as the geo-political expert Andrei Martyanov put it in his brilliant book - "to develop complex synaptic connections which are applicable for everyday life."

Some more hands-on stuff...(OR) how Linux helped Jaga Mission in Odisha

In the previous post I had started discussing about the Linux command line and the incredible flexibility and power it provides to the user. 

Using the command line, and progressing (which happens quite naturally) towards scripting and programming, also halts and reverses the "Smart when you create - Dumb when you consume" process.

The simple fact is that we can't depend on an external IT specialist or a ready-made software for most of the problems that we face regularly in our work.

Only we know the specific problems that we face in our particular work environments - and they may pop up anytime. It is impossible to out-source all such problem situations to an external software consultant.

Similarly, there may be many tasks at work, which could be solved and/or automated through the command line or scripts (a series of commands written down in a file for execution). I have already showed some examples in the previous post. 

In this post let me show another example of a slightly higher order of complexity than the ones I showed earlier.

The implementation of Jaga Mission, the flagship slum improvement project of the Government of Odisha, where I worked as a consultant urban planner, involved the creation of a pretty huge geo-spatial database.

In the first phase of the project, about 2000 slums located in 109 cities and towns of the state were mapped using quadcopter drones. The very high resolution (2.5 cm) imagery was geo-referenced and digitized to create the necessary layers of geo-spatial data layers. 

The following were the major data layers that were prepared for each slum settlement -

a) the high-resolution drone image

b) layer showing the individual slum houses

c) layer showing the slum boundary

d) layer showing cadastral (land ownership/tenancy) data corresponding to the extent of the slum settlement.

e) layer showing the existing land-use of the slum settlement

This led to the creation of a pretty substantial geo-spatial database of about 10,000 map layers. In an earlier blog on operational parameters, I have explained how this database was crucial to fulfilling the goal of Jaga Mission of granting in-situ land rights to slum dwellers. 

The geo-spatial data was particularly useful when encountered with complex situations, such as slums located on certain specific categories of land, where granting in-situ land rights may not be possible. 

When this data was handed over to the Jaga Mission office by the technology consultants, the data-sets were organised in a manner which made quick retrieval and analysis difficult.

The individual layers were stored in a series of folders and sub-folders in a manner as shown in the diagram below -

 

In order to retrieve any layer of a particular type (say, the slum household map) of any slum, one would have to first open the folder of the respective district; then the folder of the respective ULB (Urban Local Body i.e. the city) ; then the folder of the respective slum and then the necessary layer(s).

The file names of the individual layers just mentioned the type (e.g. "hhinf" for the household layer; "rplot" for the revenue plot/cadastral layer etc), without giving any further information suggesting the name of the slum or city.

While this is absolutely fine for manually retrieving the separate layer files and operating on them on a Geographic Information System (GIS) software, this method of data management is incompatible with any attempt at programming, automating or quick retieval.

And when we are dealing with 30 districts; 109 ULBs; 2000 slums; and 10000 data layer, then quick and precise retrieval is essential. Any kind of programming or process automation could also be extremely useful. 

For example, it was decided by the Government that slums located on land belonging to the Railways may need to be re-located to alternate sites. The process could be done by filtering the cadastral layers based on land ownership by the Railways and then selecting the houses which intersect with those parcels from the slum-households layer. 

However, given the manner in which the files were named and organised, this process would have to be done manually on a slum by slum basis. In the absence of an army of GIS technicians (something that the Jaga Mission did not possess), the process was bound to revert to an even more laborious process of municipal staff and revenue officials physically visiting the slums and checking if they were located on railway land.

It was almost as if the elaborate digital database had never been created.

Titusz and I wished to rename and re-organise the data-files in a manner which would enable near instantaneous retrieval and processing. But, of course, even to rename the files (in order to enable scripting), we would need - you guessed it - scripting !....or else how to rename 10000 files stored in separate folders and sub-folders ??

So, we wrote a script which would loop over each of the 30 district folders and recursively go down each sub-folder until it reached the bottom-most level where the data files where stored. 

Every time the script would move down a folder level, it would store the name of the folder as a variable. Once it reached the level of the individual file it would rename it by adding the relevant stored variables as prefixes to the original name of the file. The resultant file name would therefore contain the name of the city, the name of the slum and the type of the data layer (there was no need to add the name of the district to the file name).

The following diagram shows the concept behind the script -

 

Once this process was completed, there was no need to store the files in separate folders and sub-folders. They could be kept in a single folder and files of any combination of city name, slum name and type could be retrieved instantly.

Not only did we have fun trying to create a script that would solve our problem by making use of the names of the very folders in which they were stored (which was precisely the problem that we were trying to solve !), but we also ended up creating a fresh system which drastically reduced the time taken for analysis and decision-making regarding all future tasks.

Effectively, we used the problem to solve the problem.

As a direct consequence, it reduced the burden of manual labour which would have fallen on the shoulders of municipal workers and also reduced the problems faced by slum dwellers due to incorrect decision-making in a process as challenging as relocation.

More on those stories in the forthcoming blogs...

 
 

 

 

The importance of Operational Parameters...and the pointlessness of Data-hunting-gathering

The tragedy of data-hunting-gathering 

A defining feature of urban poverty alleviation programs in India in the present times is their ever increasing size, scale and speed of implementation. The Government plays a leading role in the planning and execution of these programs in collaboration with large (often multi-national) private sector consultants, think-tanks and non-profits with financial support from international donors and philanthro-capitalist foundations.

Increasingly, the implementation of these projects involves the creation of vast amounts of digital data, which fuels an ever growing data-hunger among all kinds of development organisations and professionals. 

Everyone is perenially looking for data these days – students, researchers, the organisation that just partnered with the government, the organisation that wants to partner with the government, the organisation that will never partner with the government, the odd travelling scholar in search of a good case study...the list goes on. 

Most of the time, the DHG (data hunter-gatherer) community is not even aware of what kind of data they need, the reason they need it for or the use they intend to put it to once they acquire it. The belief seems to be ("belief" is the right word, for there seems to be very little of science in such an approach), that once these mythical Himalayan data-sets would be acquired all the other questions would magically get answered too.

Rather than searching for data in such a hopeless manner, it could be far more useful to understand the technical aspects of the development programs that generate this data and the operational parameters they need to adhere to. Exploring in detail "how" a program is executed, also throws light on "who" actually implements it and with what tools and techniques. One can then also understand "what" data gets generated in such programs and what uses it could have.

The usefulness of studying operational parameters

Compared to the data, which may be extremely hard to obtain, the goals, objectives and  standard operating procedures guiding the implementation of these projects are more accessible. Even if they are not available in the public domain, the government is generally far more comfortable sharing these documents than the actual data. 

Having even a general understanding of the operational parameters of a project can help one deduce what kind of data may or may not have been produced as part of the project. 

For example, in the case of Jaga Mission, the operational parameters were clearly laid out in the legislation - "The Odisha Land Rights to Slum Dwellers Act, 2017" that guided its implementation.

Let’s look a bit deeper into the operational parameters of the mission in order to understand what kind of data was produced and why it was produced. 

The operational parameters of Jaga Mission

According to the Act

• A slum is defined as a compact settlement of minimum 20 households; 
• Land rights shall be granted based on the area of actual occupation; 
• The maximum ceiling for granting land rights is 45 sqm for slums located in Municipal Councils and 60 sqm for slums located in Notified Area Councils (NACs); 
• Land rights shall be granted free of charge for up to 30 sqm for families belonging to the Economically Weaker Section (EWS) category; 
• Non-EWS households shall pay a certain percentage (later fixed at 50 percent) of the benchmark value of land for securing the land rights and EWS households shall pay a lesser percentage (25 percent) for the amount of land occupied above 30 sqm.

From the above parameters, we can get a pretty clear idea of what kind of data we require in order to execute the program. It is clear that the implementation of the Mission would not only require the boundaries of slum settlements to be clearly delineated (according to the definition) but also an exact mapping of every single dwelling inside the slums in order to calculate the area under actual occupation. In its first phase, Jaga Mission covered 168000 households in 1725 slums in 109 cities, which are spread across the length and breadth of the state of Odisha (155707 sq.km), turning the mapping and survey process of the Mission into a huge logistical challenge. 

It was this massive geographical scale and the need to complete the survey within a reasonably short time which made the Government consider the use of drones. Teams of 3 to 4 professional surveyors would travel by road and reach the cities allotted to them. They would then team up with the local municipal staff and NGOs to visit the slum settlements. GIS companies and NGOs were hired and simultaneously deployed to cover all the cities and slums of Jaga. On reaching the slum, the survey team would prepare the drone flight plan (based on inputs from the municipal staff, NGO representatives and slum dwellers), set up the ground control points, fly the drone and then move on to the next slum. For an average sized slum (1.2 hectares), this process would take about 1.5 to 2 hours. After covering all the slums in a city, the survey team would pack up and drive on to the next city. The raw drone captures for a batch of cities would then be sent for processing and turned into extremely high-resolution (2 cm) ortho-images (i.e. geographically corrected images which allow true distances to be measured). While the image processing was being done, the survey teams would continue their surveys in other cities, thus ensuring a simultaneity of data capturing and data processing activities. The high resolution allowed the digitization of even the tiniest dwellings to be done directly on the ortho-image, without the need for additional DGPS surveys to be conducted in the slums (as was done in the case of the RAY project a few years earlier). 

The following three map layers (vector data) were the most crucial and where prepared for each slum -

i) Slum boundary layer – showing the exact extent of the slum on the date of the survey.

ii) Slum dwellings layer – polygons showing each dwelling unit inside the slum (along with the household survey data).

iii) Revenue parcels layer – showing the ownership details and formal records of the land parcels on which the slum is located.

Together with the ortho-image (raster) layer and an additional land-use (vector) layer, this translated into a huge dataset of 8625 map layers for the 1725 slums of Jaga.

But, once the above map layers were ready, all sorts of geographical and mathematical operations related to mission implementation could be done easily. Let's consider the following cases in a slum located in a Municipal Council (with a maximum ceiling of 45 sqm for being eligible). Let’s assume the benchmark value of land to be INR 80,00,000/acre (i.e. INR 1977/sqm).

1. Beneficiary A - EWS family occupying 27 sqm.
2. Beneficiary B - EWS family occupying 33 sqm.
3. Beneficiary C - Non-EWS family occupying 35 sqm
4. Beneficiary D - EWS or Non-EWS family occupying 50 sqm

Solution of land rights eligibility criteria for each case would be -

• Beneficiary A - Family would get land right free of cost for 27 sqm.
• Beneficiary B - Family would get land right for 33 sqm on payment of INR 1482.75/- (0.25 x 1977 x3)
• Beneficiary C - Family would get land right for 35 sqm on payment of INR 34597.5/- (0.5 x 1977 x 35)
• Beneficiary D - Ineligible until family agrees to surrender the extra 5 sqm.

We can see clearly from the above example that the operational parameters of the project as defined in the Act pretty much dictated what kind of data needed to be generated and how that data was to be used to calculate the eligibility criteria for settling the land. 

But it also becomes clear from the above, what kind of data was not generated as part of the Mission. 

The truth is in the grind

The bottom-line is that the data that governments produce for the implementation of their schemes and projects may or may not be of any use to researchers, scholars, practitioners and organisations which are not directly involved with the project. The operational parameters help us understand what kind of databases may or may not have been generated.

It is far better to have ones own objectives and questions clearly articulated and produce the datasets accordingly. In this digital age it is easy to imagine that the grind of being a solid development researcher can be avoided by just scooping up all the data that exists in government offices and working the magic of the computer on it - but that does not work.

No amount of aimless data-hunting-gathering can replace the power of scientific knowledge, well articulated research questions and a robust methodology.

The truth is in the grind.

Sorry...we cannot share the data with you !

The truth is in the verb

One of the most tragico-amusing facts that most researchers, students, consultants etc get mis-led by when going data hunting (especially for digital data) in government offices, is that when the concerned officials say, "We cannot share that data with you !" (which they almost always do) - they are telling the truth....as in literally !

For sure, they may not "want" to share it with you, but in most cases they simply "cannot".

In order to share any data - they must first have it in their possession. Well, it's a bit hard to part with what you don't have isn't it ? 

So, let's not be overly harsh on the officials of refusal, and focus instead on what are the deeper structural reasons which create this absurd situation, where the government itself is not in effective possession of its own data. And just as Thomas Piketty often says, that the structural economic problems such as wealth inequality can be better understood by resorting to the novels of Austen and Balzac than the works of economists, I would like to approach our present puzzle through the medium of personal experiences and anecdotes...and believe me, I have tonnes of them. 

Looking for data...and finding the cave of Ali Baba

Couple of years ago when I started working as a consultant for the Housing & Urban Development Department in Odisha, one of my tasks was to collect whatever data was available in the various offices of the department on the slums of Odisha. 

All through my years as a student and researcher of city planning, I was accustomed to all kinds of data collection warfare that one has to conduct in government offices - combined arms, protracted, geurrilla, psy-ops, corridor-corner-mugging...you name it - to get even the most obvious and useless bits of information. So, going data-digging with the seal of the Principal Secretary was like a walk in the park and I was quite enjoying the breezy feel of it. Even so, I didn't really expect to find anything useful at all. 

After visiting desks and cubicles, where I would be kindly directed to other desks and cublicles (I love strolling the halls and corridors of bureaucracy...it's not half as masochistic as it sounds and it has its benefits), I ended up in a corner desk in the office of the Bhubaneswar Municipal Corporation (BMC), where I struck a conversation with another consultant who was nearing the end of his contract. He seemed to remember that there was some data stored in a desktop, which was barely ever switched on. As he searched for the folder, he reminisced more, "This data was collected during the RAY scheme...then the scheme was abandoned, and no one used it anymore."

As many would remember, RAY was a promising scheme of the central government to create slum free cities which met a pre-mature end in 2014 when the Congress led UPA-II government was defeated by the BJP led NDA. While the scheme was abandoned before any serious implementation could be done, it seemed that it did generate some data before dying.

When he finally opened the folder, it turned out to be a treasure cove containing detailed shapefiles of each of the 436 slums of Bhubaneswar, organised neatly in 67 sub-folders corresponding to each ward of the city. The slum-level data consisted of separate map layers for the slum boundary, land ownership, open spaces, infrastructure and dwelling units along with the full household survey data of over 90000 households. Then there were city level map layers showing the city agglomeration area, the muncipal area, municipal wards, location of slums and the road network. From the pdf files containing the layout maps prepared for each slum, one could even find the details of the private consultancy that had prepared the maps (that's always a lucky find, as GIS databases in India almost never contain any metadata files....more on the horror of GIGO - Garbage In Garbage Out - of digital data in later blogs).

The RAY data that we discovered in that abandoned, dust-coated desktop, had been produced sometime around 2013-14. Judging by the time-stamp in the pdf files it must have been handed over to the Government in 2014.  

Data absence through Data obsolescence

Whether, a scheme gets abandoned or not, there is nothing stopping the government from making full use of such a comprehensive and detailed database and also update it in the process of using it. However, a general lack of familiarity with GIS  (although everyone keeps talking about it all the time...agreed, mostly the talk is on buzzwords and eye-candies and not on serious cartography or computing), the dependence on expensive and proprietary software (which ensures that the GIS consultants produce and process the data...and the government has eyes on it only through the consultants and for just as long as the contract period lasts) and lack of awareness regarding free and open-source alternatives creates a situation where the government just does not have the systems and capacities in place to "receive" the data even if it is "handed-over".

Well, what inevitably happens to something which you never use ??....you gradually forget that it even exists. And as the consultants (for what is the government nowadays but an army of consultants embedded in various departments and offices doing everything from preparing reports, drafting standard operating procedures, analysing data, preparing powerpoint presentations...you name it) involved with a certain project leave at the end of their contract period, the last traces of the organisational memory regarding the data disappear with them. 

It was clear from the RAY survey guidelines and the database that I discovered that the survey involved an elaborate process involving the collection of cartosat-1 (2.5 m resolution) and, the now decomissioned, cartosat-2 (< 1 m resolution) satellite imagery; differential global position system (DGPS) surveys to mark the edges of each structure inside the slum; extensive digitization to prepare the maps; detailed household surveys of over 90000 slum families etc. For sure, it must have been an expensive process too. 

And here we were, 3 years later, where the best case scenario for the data turned out to be that it was forgotten in a desktop in the corner of BMC to be accidentally discovered by me on a random data safari. If this happens in one case, then, for sure, it happens in other cases too, especially considering that the underlying causes remain mostly unchanged.

It so happened, that a few years later, when I showed that data to the then Commissioner of Bhubaneswar in a presentation, he made a sincere request that I share the data with his office as they didn't possess any GIS data on the slums of Bhubaneswar. 

So here I was in a surreal situation where BMC was asking me to share BMC's data because BMC did not possess it. 

I had half a mind to say "Sorry, I cannot share the data with you." 

Well, the trouble was that I..."could" ;))