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).

Disaggregation Dilemma - Part 2...(from static land-use color codes to something like OSM features indexing)

Information density at lowest levels of disagregation

Let us continue the discussion on information loss and data aggregation by comparing the maps of different planning levels that I showed in the first part of the blog, with their corresponding scales on OpenStreetMap and google satellite imagery. 

Instead of starting from the top (the city level), let's start from the bottom (the layout level) this time, and remember the words of Prastacos again- 

that computerisation basically allows us to maintain data at the lowest level of disaggregation and then readily aggregate it as the need arises.

There is of course no restriction on further zooming into the osm (OpenStreetMap) or the satellite imagery to study the area on greater detail. Similary, one can zoom out and reduce the scale to any extent to study larger areas. One does not have to stay restricted to certain categories of pre-defined map-scales (and needless to say, we also get our freedom from the scanned copies of water-soaked blue-prints that the government generously shares as "open data" and get a feel of the power of the REAL open data).

 

What is information loss due to aggregation ?

If we zoom into the area of the layout plan in the City level land use plan, then this is all the detail that we could possibly get -

 

Now compare the above level of detail (left) with what we saw in the case of the osm image (right) -

 

The above comparison is a simple visual representation of the amount of information loss that happens when spatial data is aggregated to higher levels using non-computerised cartographic methods.

There was simply no other way - in the absence of computers - than to prepare maps of different scales; covering different geographical extents in order to show different planning levels.

However, none of those limitations remain if one is working with digital spatial data and computers - there is no information loss at higher levels of aggregation of the same map. 

The trouble is that we continue to operate with the same methodology even when we have computers and geo-spatial software at out disposal.

Coding spatial information - learning from OSM features

When one understands the fundamental manner in the way in which computerisation allows aggregation and disaggregation of data, then one can also understand that the manner in which land and building uses were coded in earlier non-computerised map-making systems are no longer adequate or relevant.

Incidentally, a very powerful and effective alternative to older methods of land-use coding has already started appearing in the form of the "Map Features" of OpenStreetMap. 

Here is a description of the system from osm's wiki page -

OpenStreetMap represents physical features on the ground (e.g., roads or buildings) using tags attached to its basic data structures (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by that specific node, way or relation.

Most features can be described using only a small number of tags, such as a path with a classification tag such as highway=footway, and perhaps also a name using name=*. But, since this is a worldwide, inclusive map, there can be many different feature types in OpenStreetMap, almost all of them described by tags.

The osm feature indexing system is extremely thorough and exhaustive and designed for use by the computer. Have a look at the difference between a typical color coded land use system and the osm map features system below.

This is how land uses are color coded in a typical land-use plan -

And this is how the osm map features indexing system looks like -

Just a casual glance is enough to see the power of this feature indexing system. It lists the various types of uses as key-value pairs, states what osm map elements they belong to, provide a clear description of each feature, the rendering and also photographs of typical examples.

The wealth of information that gets collected and maintained using such an indexing system is truly mind-boggling.

The analytical opportunities such systems open up can help us go toe-toe with the most complex urban problems that we face - and win.

 

Disaggregation dilemma - Part 1...(Of GIS based PDFs and Water Soaked Blue-prints)

What is wrong with our land-use plans ?

Well...nothing. Except, perhaps, the fact that they belong to an earlier epoch of technological development - a period when one necessarily had to prepare maps at different spatial scales in order to show greater or lesser detail; and use specific colours to aggregate the primary land uses at different scales - for example, yellow for residential use; red for commercial use (depending on prevalent cartographic rules).

One can also say that the technology of land-use maps, as they continue to be used to urban planning in India, corresponds to the period of map making prior to the advent of computerised cartography and geo-spatial analysis.

Using present technology, we do not need to switch between different maps prepared at different scales to study different degrees of spatial detail. Instead, we can simply zoom in and out within the same map. 

In most aspects of our lives we take this for granted - when we are booking an uber; or checking directions to a destination on google maps; or checking how far the swiggy delivery partner is at a particular point of time. 

In all such businesses, computerised geo-spatial analysis and decision-making is not just one of the components to be considered -  it is the most fundamental science and technology on which the business operations play out.

However, in a vital and complex activity such as urban planning, whose social and economic significance far exceeds that of profit maximisation in the gig economy, such technology is still a sort of a novelty which is far from having been internalised by the rank and file of the profession.  

In fact, the inadequacy of technical knowledge becomes amply clear precisely when one takes a look at the manner in which the planning profession attempts to internalise geo-spatial technologies. I discussed this in an earlier blog.

It is perhaps too difficult for our planning professionals and educators - too busy flaunting tech-terms and buzzwords - to come to terms with the simple fact that if your planning maps are made using GIS software then you do not need separate sets of maps at the levels of the city - i.e. the city level, the zone level and the layout level -- they are all part of the same geo-spatial database ! 

I am not even getting into the travesty of making such "GIS" maps available online in PDF format and then providing the attribute data in separate spreadsheet files and THEN announcing this pointless hotch-potch as Open-data ! A tighter slap on the face of the open-data movement was never landed. This is not open-data...this is an open disdain of the citizen.

 

From GIS based PDFs to Water soaked Blueprints

Let's have a look at such maps as they are available from the website of the Delhi Development Authority -

a) Here is the "big honcho" - the proposed land-use map of all Delhi. The highest level of the plan and the one with the smallest geographical scale and level of detail. Most of the time lay-persons attempting an analysis of the Delhi Master Plan remain pre-occupied with this level. Of course, it shows nothing more than the most general and most aggregated land-use distribution at the level of the city.

b) The next level of planning detail comes in the form of Zone level land-use maps. Shown below is the map of Zone-F in South Delhi. As per the zonal plan report, already in 2001, this zone had an area of 11958 hectares (i.e. 119.5 square kilometres) and a population of 12,78,000. That basically means that while it is just a part of the city of Delhi, it is still larger than many smaller sized cities of India (it is, in fact, larger than the smart city of Bhubaneswar in terms of population). 

The Zone too, therefore, is at a substantially high level of aggregation and can be compared to city level land use plans of one-million plus cities in India.

(NOTE - pay attention to the key-map in the attachment below and marvel at the cartographic genius of whoever prepared this "GIS based" pdf output)

c) And something peculiar happens when we go down to the level of the layout that contains the maximum geographical detail - the layout plans; which are more like a plan for a cluster of neighbourhood blocks. 

Here is what the plan of one of the layouts constituting Zone-F looks like...if you can make anything out that is. The keen observer would realise that this is actually a well drafted layout map (at least the key map is correct !), but we have suddenly descended from the world of GIS based PDF map outputs, to the world of water-soaked and worn-out archives of crumpled gateway sheets and blueprints. 

This is what gets uploaded as digital layout maps on the website of the premier urban planning agency of the capital of the country. 

There is therefore a complete dissonance between what digital and geo-spatial technologies truly are and how they are being utilised. 

In this matter the critics and activists of the civil-society and consultants of the private sector are often more technically incompetent than government planners. The government officials may not be familiar with the modern software but they know their cartography well enough (as illustrated by the water-soaked map), while civil society critics and private sector consultants (who often actually prepare the "GIS" outputs) are often poor in both technology and cartography.

 

In the next part we will see how computerised geo-spatial methods eliminate the problems of aggregation by allowing data to be maintained at as disaggregated a level as allowed by its granularity and aggregating the base-data as per requirement to whatever level necessary processing power of the computer.

In the words of planning expert and theorist Poulicos Prastacos -

"Data should be maintained at the lowest level of disaggregation and then readily aggregated as the need arises."

(Source - 'Integrating GIS technology in urban transportation planning and modeling' - P. Prastacos)

To be continued...

The Data exists...right under our Mouses !

The capital irony

It is perhaps a capital irony of our times that precisely at the time when computers are more powerful and affordable than ever before and the access to powerful and previously expensive software provided by the  Linux + FOSS (Free and Open Source Software) movement, the general ability to use computers effectively to address the various problems faced by our cities is at an all time low.

I myself come from a background of primarily qualitative and participatory techniques in urban planning. I continue to have a natural fondness for such techniques, but have increasingly also discovered the power that effective use of computers bring to my work.

Contrary to the myth that the quantitative and qualitative worlds are poles apart (which leads to the further myth that professionals dealing with qualitative techniques cannot use computers for serious quantitative analysis), the two are in fact friends and allies of each other and help each other continuously.

Without waxing complex, think of a rather simple example. I would like to undertake participatory exercises in various slums in my city and I use all kinds of creative ideas to undertake the same inside those communities.

But alongside that, I could also prepare a GIS database of the slums in the city that gives me spatial and quantitative information on slums - such as their location, distance from each other, distance from other city facilities, size and density of the settlements, the population and occupational characteristics of the settlements etc.

This quantitative database can actually help me increase the effectiveness of my qualitative techniques by helping me to schedule meetings, use different techniques in slums of different sizes and shapes, check the probability of consensus-building (fewer meetings could build consensus faster in a smaller slum than in a larger and denser slum) etc.

Rather than focusing too much on whether to deploy quantitative or qualitative methods, it is better to focus on the problem that needs to be solved and deploy whatever methods that may be necessary.

Why computers ?

As long as I want to do participatory activities in a handful of slums, I may not need any support of computers at all. However, if I would like to undertake such activities in tens or hundreds or thousands of slums, then I begin to feel the need of the processing power of the computer.

It is as simple as that.

As more and more resources are made available to various urban development programs and schemes in India, their sizes, duration and scale of operation are all increasing. It is not difficult to understand that in a country the size of India, urban development projects would need to be undertaken at a scale where one can at least hope to make a meaningful difference. 

But of course, computers need instructions to follow - and they need data to work on.

The data exists...right under our mouses

Quite often, the impossibility of obtaining data is cited as one of the main barriers to effective use of computers in solving urban problems in India. I have written on this topic on multiple occasions. And I have stressed on earlier blogs that mere accumulation of digital data is not of much use if one does know how to use computers effectively to process it.

However, another capital irony of our times is that much of the data whose absence we so lament - does indeed exist...and sometimes right under our noses (or mouses).

Let me demonstrate.

This particular link will take you to the dashboard of the "GIS based Master Plan" sub-scheme of AMRUT. 

The very first component of this sub-scheme was geo-database creation and in the following screen-shot of the dashboard we can see its status -

 

If we look at the first three steps of the component, we can see that satellite data had been acquired and processed for about 450 cities. In the pie chart on administrative works is not self-explanatory, but it could mean that the National Remote Sensing Centre (NRSC) of the Indian Space Research Organisation (ISRO) may have handled the satellite data acquisition and processing for 240 cities and private companies may have done it for another 220 cities.

In any case, according to the official dashboard itself, we can conclude that processed satellite data exist for about 450 cities. As per the status chart, final GIS maps also seem to exist for 351 cities.

From if it exists...to where it exists

Finding evidence and clear arguments for the claim that something exists, is the first step in finding something. If I know for sure that something exists, then I need not succumb to the fallacy that it doesn't even exist. 

The task after that is to discover, where it exists, rather than wonder if it exists.

The same method can be applied to understand exactly what all data has been collected and processed under the myriad central and state government schemes that are going on in the country and have already been executed in the past.

Believe me, we will have more data than we would need for getting most of our tasks done.

The catch here is this...a person who does not have the imagination to discover the data, most likely would not have the imagination to use that data either.

But let's keep that blast for a later post ;)

Indian Space Assets and Urban Planning

Smart in Space...Clueless on Land

As India's capabilities in the field of space technologies increase continuously, the gap between the data generated by our space based assets and the utilisation of the same for solving pressing social and economic problems is felt palpably...and painfully.

After all, the vision of our space program has always been to -

"Harness, sustain and augment space technology for national development, while pursuing space science research and planetary exploration."

And this is the level that we have already reached in this domain -

 

When it comes to space, we are not just good - we are among the best in the world and sometimes better.

The Indian satellite cartosat-3, launched in November 2019, is one of the most advanced high-resolution earth observation satellites in the world. 

With a resolution of 25 cm, Cartosat-3 surpasses the American World View-3 satellite owned by Maxar Technologies, which has a resolution of 31 cm.

And guess what is written in the Mission document of Cartosat-3 as the primary application of this third generation satellite -

You can access the document on this link.

One would expect that such high resolution products would be developed for the defense sector alone, but we have reached a level of technological development, where the mission document lists purely civilian sectors as the target users of Cartosat-3.

I seriously wonder whether the scores of professionals of India's urban development sector are aware that one of the most advanced products of one of the most advanced fields of human technology has been produced by their country for them.

Is it really so hard to imagine, what all becomes possible when you have high resolution satellite data available for the whole city and the region ?

From data collection to serious data analysis

With the development of advanced earth observation satellites we can finally take a pause from the gigantic spatial survey exercises that take up all the energy and creativity that could and should be dedicated to data analysis, forecasting, modelling etc. In any case, the fragmented and project specific data generated by these large urban development projects is used poorly and then abandoned and forgotten the moment the projects come to an end (refer to previous blogs for more details).

Consider the fact that Jaga Mission - which created one of the largest high-resolution geo-spatial database of urban slums in the world, had to deploy three separate drone survey companies, multiple quadcopter drones, teams of surveyors and the resources of over a hundred city governments to complete that survey in less than a year.

Given the logistics of covering almost 2000 slums in 109 small and medium cities spread across the length and breadth of the state of Odisha, which has an area of 155,000 square kilometres, it was a daunting task. Typically, in large urban development projects in India (and they are all large nowadays), so much energy and resources are devoted to conduct the surveys and create the datasets that serious analysis never gets a chance to take off. 

Such a daunting logistics would also imply, that while the urban reality is extremely dynamic, the probability of repeating such a spatial survey exercise at regular intervals would be very low.

From the point of view of temporal change, the ultra-high resolution data collected under Jaga Mission in 2018 may already be out of date. And there exist no plans of updating that data set.

Satellite data has no such problem with temporal resolution (the interval of time after which the same area of the surface of the earth would be captured again by the satellite in orbit).

With Cartosat-3 data, one can not only get a detailed picture of slum settlements (not just in Odisha, but through the country), but one can also analyse the spatial changes over time and also relate the location of slums to other features in the city (none of these are possible with Jaga Mission drone data, which only captured images of individual slum settlements.

Data without scientific knowledge is useless

There is a never-ending clamour for data among urban development professionals and researchers in India...a tendency I described as "data hunting-gathering" in a previous blog. However, it was not access to unlimited amounts of data that made the Indian space program what it is today - but scientific knowledge and the intelligent application of that knowledge.

Data is crucial - but only when one possesses the necessary scientific knowledge to effectively use that data. 

The trouble is that many (definitely not all) professionals demanding access to all kinds of digital data - i) would not be able to recognise that data if it were staring at them from their computer screens; ii) would not know what to do with the data even if by some miracle they figured out that it was indeed the data that they were looking for.

The usual excuse given by scholars and practitioners alike is that urban challenges are too complex. Of course, they are complex - but complexity of a problem is as much a function of the knowledge of the problem-solver as it is an intrinsic characteristic of the problem itself.

Finding an address too can be a very complex task - if someone doesn't know how to read a map. 

It is always nice to check whether a problem is really complex or if I am too dumb ! It may be very nice to discover that it is the latter, because then I know that the problem is solvable and I also get an opportunity to study and learn something new and useful.

Unless that knowledge gap is bridged, the brilliant developments in the field of Indian space technologies shall be unable to solve the relatively mundane challenges of urban planning and development.

And that would be a real shame.

You cannot "find" data, if you cannot recognise it

I have written in earlier blogs about the trouble with GIGO (Garbage-In-Garbage-Out), which basically means that if the data that goes into your software is garbage, then your output would be garbage too. However, sometimes the problem may be what Andrei Martyanov describes as "double-GIGO" (I can't remember which video it was in but feel free to check out this one anyway), where both the data used and the formulation of the problem may be garbage. Needless to say, that it is one hell of a tragedy when that happens. Unfortunately, it is not a rare phenomenon at all.

However, quit often, the problem is simply not knowing what data to look for and where to look. For example, last year the CITIIS program of the National Institute of Urban Affairs (NIUA) organised a training workshop for the Department of Housing and Urban Development in Odisha for mapping of water bodies in urban areas of the state. The program started with the mapping of 19 water bodies in the pilot phase.

Having engaged with the implementation of such programs from the inside, I know for a fact that "there is many a slip betwixt the cup and the lip", when it comes to their objectives and the data that they use for achieving it. I have written extensively on the state of data in government projects in multiple blogs.

Even without going into any detail of this specific program, isn't it a bit peculiar to do such a gala workshop on such a theme, when the following already exists ?? 

This is the dashboard of the Water Bodies Information System (WBIS) of the Indian Space Research Organisation (ISRO). Here is the link to it. 

The site clearly mentions -

"The water spread area information is extracted using images of 6 m to 56 m resolution for delineating water bodies of sizes as small as 0.025 ha and 50 ha respectively. This information is made available as Water Bodies Information System (WBIS) for visualisation and download."

One would expect that the organisers of the workshop to be atleast aware of the existence of such an information system prepared by such a respectable organisation - Alas, nothing of the sort !

When the knowledge and technical abilities of urban development professionals increasingly gets limited to preparing powerpoint presentations, having endless online meetings and organising various kinds of "capacity-building" events, then the probability of being able to identify relevant data (even when it stares at you point blank) is bound to decrease...and at a steep slope.

One can safely argue that it is hovering somewhere very close to zero if not already there

The ability to find data is very much a function of being able to recognise relevant data and that in turn is dependant of ones knowledge of the subject, which needs constant updating.

But is there any time left for that after the relentless "labour" of back-to-back online meetings ? 

Regarding farcical flirting...and GIS based master plans

Earlier today, I wrote the following on my linkedin status -

GIS based Master Plans...it's a bit like saying pen based novels...or camera based photographs.
India's farcical flirting with technical terms has to stop.
It shows an unreflected acceptance of meaningless sentences - in other words, it helps accelerate collective stupidity.

I felt that this should be elaborated upon. 

To be sure, I am not against flirting - it is a creative art. Anyone who wishes to study it seriously could turn to Act 5, Scene 2 of William Shakespeare's play "Henry V". 

Here is a youtube link to the scene in the classic film adaptation by Sir Lawrence Oliver -

 

Of course, one is free to point out that strictly speaking Henry was wooing Lady Katherine and not flirting with her. 

But then I am equally free to reply, that while Henry was indeed wooing Katherine...he was also flirting - not with her - but with the just altered geo-political situation in Europe following the battle of Agincourt where Henry achieved decisive victory over the French and was in a position to dictate terms to her. 

Yes, flirting is art indeed - of geo-political scale and significance.

So much for my admiration for genuine (geo-political or not) flirting. 

But the recent tendency (increasing at an exponential rate) in India to flirt with "tech" terminology (including the ridiculous sounding word "tech") without any regard for what they really mean, can be considered farcical indeed.

Sentences beginning with the following should immediately put one's BS-filtering systems on high alert -

• "We are developing digital tools for..."
• "According to our AI based tools..."
• "As per our machine learning algorithms..."
• "We use high-resolution satellite imagery for..."
• "We have adopted a data-driven approach for...."

The trouble is not with terms like digital, AI, machine learning, high-resolution satellite imagery etc., but with the things that generally appear in the second part of the sentence.

Consider the following statement - "We use high-resolution satellite imagery to track land-use violations in Bhubaneswar smart-city on a monthly basis."

If this video clip (with an animation showing a satellite "diving" from its orbit every time it tries to get a better look at Bhubaneswar and other such wonderful things) is not enough to turn you numb, consider the following questions -

• How exactly does a high-resolution satellite imagery help me understand what use the buildings it shows are put to - commercial, residential etc. ?
• How does it help me understand violations in bye-laws such as height restrictions ?
• How exactly does it help me understand the prescribed use of the land on which the building is situated ?

 

 AND...

• What about the fact that last I checked the master plan of Bhubaneswar was under revision the new one is not even out in the public yet ??

High-resolution satellite imagery is indeed extremely useful for city planning purposes, but let's just say -

there is many a slip 'twixt the cup and the lip.

 

In other words, there is a whole range of activities that have to be done, and done properly, before high-resolution satellite imagery can perform the task of tracking and preventing land-use violations.

By beginning the sentence with the 'cup' and ending it conveniently with the 'lip', without ever clearly mentioning what all lies in between betrays either complete ignorance of the processes involved or a sly ploy to shock-n-awe anyone not familiar with such technology for the sake of furthering ones agenda.

As regarding all that lies in between the cup (the technology) and the lip (its successful application), we can turn to Shakespeare again and say,

"Ay, there is the rub !"

It is precisely in all the activities that need to be done to make a technology effective, that the real professional grind lies - and that which most would like to steer clear of.

It is easy to write a software...but notoriously hard to organise and clean the data that would be used by the software; and fix the organisations that would use the software.

 

And now let's turn to the star of the show...one that really cracks me up every time I hear it - 

GIS based Master Planning.

I wonder who came up with that one, and most importantly - why ?

Do we ever say ludicrous things such as a pen-based novel; or a camera-based photograph; or a type-writer based article  etc ?

What exactly is the point of defining a planning process using one of the many tools, which may be deployed to aid its preparation, apart from either or both of the following -

    a) Zero understanding of planning.

    b) Zero understanding of the role of GIS in planning.

Well, there is a set of more realistic causes which are far more sinister than the above two - but let's go with these for the moment.

Applying the GIGO (garbage-in-garbage-out) model - which suggests that if the input (data) is garbage, then the output (solution) would be garbage too - to our present topic, we can argue that if the input (the planning approach) is meaningless, then the output (plans produced), would be meaningless too.

Thankfully, we have an elaborate dash-board available in the public domain to support our argument. 

As expected, the dash-board is a cool and elaborate one containing all kinds of information, except the most important one - the plans themselves. 

The first alarm bell rings when we scroll to the middle of the page and look at the master plan formulation status. We learn, that after 8 years of implementation, only 135 out of the total 500 cities covered by the scheme have reached the final step of "Final Master Plan". A total of 257 have reached the level of "Draft Master Plan".

But where are these amazing 135 GIS based Master Plans ?

For that one has to scroll right to the bottom of the page and click on the relevant states (or cities) on a map to access the plans. One has to repeat a few more rounds of needless clicking until one finally reaches the page from which two separate files can be downloaded - Master Plan Report and Land Use Map.

As Gujarat was proudly colored green (all tasks completed for all cities), I decided to click on a random city called Botad.

I didn't expect to hit jackpot on the first city I clicked on, but this is the Master Plan Report that popped up -

 

 You can download the report directly from this link.

It was a pdf copy of a 30 page long report in Gujarati language. And here is a screenshot and direct link to the land use map that accompanied it -

The map too is in pdf format; a jumble of different layers overlaid on top of each other; with no clear distinction between existing and proposed land-use. 

The map is a cartographic nightmare but that's the least of our worries.

Forget about using available technology to develop a planning approach based on decades of theoretical and practical advances and reflections in the field of planning, we are offered a national level scheme which purports to use GIS but provides us with pdf files that tell us nothing and which we can use for nothing.

The reports and plans of different cities seem to be prepared by different private consultants, each following their own cartographic rules; structure of report; and occupying their own unique positions on the scale of being slapdash.

The only thing that we can be certain of regarding the meaning of the term "GIS based Master Planning" is that all consultants have made some use of GIS software in preparing the maps.

Pardon my French, but what the F*** is the use of that ?

And yet, why is it that we don't burst out laughing and brush aside the moment we are presented with these ludicrous "tech"-loaded planning terms, be it GIS based planning or the galaxy of terms following that other ubiquitous and incomprehensible adjective - Smart ?

What makes us take such tragic farces seriously....discuss them, develop projects around them, organise conferences and webinars on them ?

May be because we are distracted from the real tasks that face us...and alienated from the scienctific knowledge that we need to tackle the urban challenges facing us.

This nonsense seems to be everywhere...and relentless.

But it seems to contain the petrified brittleness of all things insecure...one tiny push and it may crumble to dust.

Then why not give it a resounding whack ?

 

 

   

 

 

 

 

 

Solving the "land" variable in land-titling equations (and some more on tackling complex urban problems)

My previous blogs have stressed the importance of operational parameters of large and complex urban development projects and how their implementation can become very difficult - if not impossible - without a creative combination of modern computing and community feedback. Perhaps nowhere does this reality hit home harder than in the implementation of mega-sized slum land-titling and upgrading initiatives such as Jaga Mission.

The Odisha Land Rights to Slum Dwellers Act, 2017, which guides the implementation of Jaga Mission, states in section 3, sub-section 1, that 'every landless person occupying land in a slum in any urban area by such date as may be notified by the State Government, shall be entitled for settlement of land and certificate of land right shall be issued in accordance with the provisions of the Act.'

All very well-intentioned and clear so far, but a whole operational quagmire opens up when one begins to implement the provisions of the Act. The art of land administration in India as it exists today, is an elaborate one that pre-dates the Mughal era. Words like 'kissam' (a derivative of the Farsi 'Qism', referring to land-use type) co-exist with the English 'Record of Right' in land revenue records, reflecting the deep and layered history of the subject. The typical planning student of professional is often not even familiar with these terms used by the revenue department, let alone being able to intervene in the operational matters of land administration.

The slums in any city may be located on hundreds of separate parcels of land, which may belong to diverse 'kissam' types, which, further, could be a mix of non-reserved (on which land rights can be given) and reserved (on which land rights cannot be given without initiating a process of re-classification of land in consultation with the revenue department) categories. Furthermore, parcels belonging to different kissams could be owned by an array of government departments or private entities, depending on which it may or may not be possible to grant land rights. It is these attributes that must be cross-referenced with each other and with the location and household data of the slum dwellers in order to satisfy all the conditions necessary to settle land rights.

Quantifying Complexity

Let's take the example of a single city of Balasore in Odisha. The 3128 families living in 41 slums of this city are located on 735 land parcels with belong to 33 different 'kissams'.

Let's further consider a particular 'kissam' called 'gharbari', which refers to homestead. There are 238 parcels corresponding to this kissam out of which 121 parcels are owned by private enitities; 71 parcels are owned by the railways; 23 parcels are owned by temple trusts and 23 parcels are owned by various departments and offices of the state government. Only the slum houses located on the last 23 parcels in the above list, can be settled without getting into special arrangements and negotiations with other government departments and private entities.

This is just one kissam out of 33. Now, also consider the location of the 41 slums in the city and how the 3128 slum houses intersect with the land parcels of various kissam and ownership types. 

What has been described above is the case of just one city out of 115; just 41 slums out of 2919; and a mere 3128 slum houses out of a total of above 400,000.

This is complexity quantified.

And this is why we need the processing power of modern computers (and not for making powerpoint presentations on the achievements of the Mission).

Political will is a necessary condition...but not a sufficient one

This is also the reason why in the first phase of Jaga Mission, which covered about 170,000 slum households in 109 small and medium towns of Odisha, the government reached as impasse after granting about 70,000 land rights certificates. The remaining 100,000 households fell on land parcels belonging to various reserved kissam categories, restricted central government lands, private entities, temple trusts or environmentally hazardous lands.

Instead of basking in glory for having distributed 70,000 land rights certificates in less than two years (no mean feat !) the Department of Housing and Urban Development, the nodal agency overseeing the implementation of the Mission, went out of its way to initiate inter-departmental negotiations with the revenue department, forest and environment department, private entities such as royal families, temple trusts etc to find workable solutions to grant land rights to the remaining slum households. Special standard operating procedures were also developed to address the challenge of slums located on highly restricted central government lands such as those belonging to railways and defence, which may require relocation.

The special measures initiated by the Government were a clear indicator of the strong political will that backed Jaga Mission. It was also a clear indicator that even when a strong political will exists (which is rare in itself), the implementation of any large pro-poor intervention may face serious challenges due to technical and operational reasons.

And it is extremely important for people not directly involved with the implementation of such projects to have a thorough understanding of these operational reasons if they wish to engage effectively and critique accurately.

Consider this news clip (in Odia) from 2019, by a regional news channel which was critical of the Mission. It showed the residents of a slum called 'Godhi Basha', who had not received land rights certificates. However, the news anchor could not give any reason for the state of affairs apart from the usual one that the government was failing to keep its promise to slum dwellers. At the 33 second mark, the clip showed a beneficiary called Ms. Anjana Das holding the card showing her Jaga Mission house number.

However, as we have already seen, lack of political will is definitely not a problem with Jaga Mission. What then is the mystery of the Godhi Basha slum ?

Had the journalist investigated just a little more, he would have discovered that the whole slum was situated on a parcel of land that belonged to "South Eastern Railways" - one of the 72 land parcels in the city which are owned by the Indian Railways. As a matter of fact, the journalist also got the name of the beneficiary wrong.

It was possible to identify all these issues in time less than the duration of the news report, thanks to the digital data collected as part of the Mission, relevant open-source software and quick data analysis on the command line, which we started discussing in the last two blogs. 

Unfortunately, as I have shown through case examples in another blog, the government itself fails to utilise available computing technologies effectively by continuing to rely on archaic bureaucratic methods and pointless application of manual labour (relying on paper maps and field visits despite possessing high-resolution imagery and GIS databases).

It definitely retains operational overview but it cannot solve the complex problems (where complexity is merely a function of processing power available i.e. with respect to the computer, they are simple problems) which not only prevent it from overcoming its operational impasses but also cause community level confusion as shown in the case of the news clip.

Community Empowerment at Slum level...Community dis-empowerment at Mission Level

Just as the Government faces its own difficulties for not using available technologies effectively, so does the community.

As I have pointed out earlier, given the size and complexity of projects such as Jaga Mission, the typical methods of community participation, engagement and critiquing are simply not adequate. 

It is not enough to understand why something is not working in one's own slum - it is only one out of 3000 slums ! 

That means 0.0003 % of the Mission in terms of the number of slums.

How can the community get effective overview of the implementation of the Mission while covering (at the level of each slum) a microscopic 0.0003 % of the Mission, when the Government and its consultants have overview of 100 % ?

Just have a look at the maps above. The one on the left, shows the location of Godhi Basha among all the slums in Balasore city; and the one on the right shows the location of Balasore city among all the cities covered by Jaga Mission. 

I hope this gives some sense of the scale....in other words, what 0.0003 % looks and feels like.

Unfamiliar methods...or merely abandoned ?

This is why it is neither enough for governments to continue using their familiar bureaucratic methods, nor is it enough for community organisations to continue using their familiar participatory development methods. 

Probably it is time to embrace the unfamiliar methods.

The size and complexity of urban challenges in the present times demands the use of methods which were created specifically for tackling large, complex and dynamic systems -- more extensive use of open-source geo-spatial software and data analysis; further refinement of mathematical models for urban and regional planning; application on the principles of cybernetics to understand the functioning of urban systems; techniques of operations research etc. 

It is an irony that huge progress was made in the development and application of many of the above techniques in solving pressing social problems precisely when computing power was very low, and abandoned in favour of feel-good but ineffectual qualitative approaches (not to mention the pseudo-scientific farce that passes for 'tech' in contemporary urban discourses) when computing power is at its peak.

More on that in forthcoming 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...