I have a lot of opinions on data, cities, and maps, but I am an introvert. I probably won't talk much when we meet, so I find it easier to share my ideas here. If you want to know what I'm actually thinking, just read on.
I have a lot of opinions on data, cities, and maps, but I am an introvert. I probably won't talk much when we meet, so I find it easier to share my ideas here. If you want to know what I'm actually thinking, just read on.
The "Data Denied" Email: Why Public Data Remains Locked Behind Private Doors
We have all been there. You are scrolling through LinkedIn or reading a technical journal, and you stumble upon a fascinating piece of analysis. Maybe it is a heatmap of urban heat islands in your city, a detailed study of bus route efficiency, or a hydrological model predicting flood risks in vulnerable neighborhoods. The visualization is crisp, the insights are profound, and your mind immediately starts racing with possibilities.
“If I could just overlay this with my demographic data,” you think, “I could prove a correlation that no one else has seen.”
So, you draft an enthusiastic email to the author. You compliment their work, explain your research interest, and ask the golden question: “Could you please share the raw shapefiles or the dataset used for this analysis?”
You wait. A day passes. Then, the reply lands in your inbox. It is polite, professional, and utterly defeating:
“Thank you for your interest. Unfortunately, we cannot share the raw data. This dataset was provided to us by [Government Agency X] under a specific agreement for this project only. We are not authorized to redistribute it.”
That sentence is the sound of a door slamming shut on innovation. It is a frustrating reality for geospatial professionals, urban planners, and researchers everywhere. But what does it actually mean? Why do government agencies decide that only certain organizations get to hold the keys to the kingdom? And why does this make platforms like OpenCity not just useful, but essential for democracy?
Decoding the Rejection: What "Restricted Access" Actually Means
When an author tells you they "can't" share the data, they are usually telling the truth. They aren't hoarding the data out of malice; they are bound by legal handcuffs known as MOUs (Memorandums of Understanding) or NDAs (Non-Disclosure Agreements).
When a government agency—be it a transport corporation, a water board, or a municipal body—shares data with a research institute or a consultancy, they rarely view it as "releasing" the data. Instead, they view it as lending the data.
This "lending" comes with strict conditions, usually defined by Purpose Limitation.
The Clause: The agency stipulates that the data is to be used solely for the deliverable defined in the contract.
The Liability: If the researcher shares that data with you, and you find an error, or publish something critical of the government agency, the original researcher could face legal action or be blacklisted from future government contracts.
Essentially, the data is treated as a state secret rather than a public asset.
The Gatekeepers: How Agencies Decide Who Gets In
If the data exists, and it is useful, why is the default answer "No"? The decision-making process regarding who gets access to government data is rarely transparent, but it is usually driven by three invisible factors:
1. The Culture of Risk Aversion
Bureaucracy thrives on the status quo. For a government officer, releasing data carries risk, while withholding it carries none.
If they release data: Someone might find mistakes in their records, expose inefficiencies, or use the data to criticize policy. This is a career risk.
Security is the ultimate trump card. In many countries, high-resolution maps or utility network data are classified under vague "national security" protocols. While protecting critical infrastructure is vital, this argument is often overused to hide mundane datasets—like bus schedules or tree censuses—that have no security implications whatsoever.
The Cost of Silos
The result of this gatekeeping is a massive duplication of effort and a stifling of innovation.
Imagine five different PhD students in the same city. All five need a map of the city's storm water drains.
Student A has connections and gets the data.
Student B pays a bribe or a hefty fee to get the data.
Students C, D, and E spend six months manually digitizing satellite imagery to create a version of the data that already exists.
This is a colossal waste of human capital. Instead of solving the problem (flooding), we are wasting time recreating the baseline data. Furthermore, because the official data is never scrutinized by the public, it often remains riddled with errors that go uncorrected for decades.
The Antidote: Why Platforms Like OpenCity Matter
This is why platforms like OpenCity (and the broader Open Data movement) are not just repositories; they are acts of resistance.
When platforms like OpenCity, GitHub repositories, or OpenStreetMap communities liberate data, they bypass the gatekeepers. They do this by:
Filing RTIs (Right to Information): Legally forcing agencies to release data that should be public.
Scraping and Cleaning: Taking messy, locked PDFs and converting them into machine-readable formats (CSV, GeoJSON) that anyone can use.
Crowdsourcing: When the government refuses to map the bus stops, the community goes out and maps them manually.
The Shift in Power
When you download a dataset from an open platform, you don't have to sign an NDA. You don't have to promise to only write "nice things" about the government. You are free to analyze, criticize, and innovate.
For the Student: It means skipping the 6-month data collection phase and going straight to analysis.
For the Citizen: It means being able to verify if the road meant to be built in front of their house was actually sanctioned.
For the City: It means that instead of one consultancy thinking about urban problems, you have thousands of minds working on solutions simultaneously.
Conclusion: Demanding Data Democracy
The next time you get that "Data Denied" email, don't just sigh and move on. Let it remind you why the fight for open data is so critical.
Data is the soil from which solutions grow. If the government walls off the soil, they are starving the ecosystem of innovation. We need to move away from a system of patronage—where data is a favor granted to the few—toward a system of rights.
Until government policy shifts from "Restricted by Default" to "Open by Default," platforms like OpenCity are the lighthouses in the dark. Support them, contribute to them, and keep asking the hard questions. Because an analysis that cannot be verified, replicated, or built upon isn't science—it's just a pretty picture.
AI Disclosure: The blogpost was written and edited with assistance from Gemini Pro
In many parts of the world—across Europe, the USA, and the UK—GIS analysis often begins with an abundance of structured, accessible data. Open datasets, APIs, versioned spatial layers, and well-maintained metadata are taken as a given.
In Indian cities, this is rarely the case.
Does that mean analysis stops?
No.
It means analysis starts differently.
Urban problems in Indian cities are visible long before they are measurable. Flooded streets, broken footpaths, informal transit routes, missing drains, and uneven service delivery don’t wait for perfect datasets to exist.
When data is missing, the question is not “Can we analyse this?”
The question becomes “What data already exists—and how do we use it to demand better data?”
Analysis, in this context, is not just about insight.
It is also about assertion.
In data-scarce environments, analysis often begins with fragments:
Administrative boundaries that don’t quite align
Old satellite imagery
PDF maps and scanned drawings
Citizen complaints and photographs
Field observations and GPS traces
Local knowledge that never made it into a database
These are not “dirty substitutes” for real data.
They are evidence.
GIS here becomes a tool for stitching together partial truths—overlaying, cross-checking, validating, and questioning.
When you map what exists, gaps become visible.
Missing attributes become questions.
Inconsistent boundaries become governance issues.
Outdated layers become proof of neglect.
This is where analysis shifts role:
From decision support
To accountability support
Maps are no longer just outputs—they become arguments.
They help ask:
Why is this infrastructure unmapped?
Why does this dataset stop at ward boundaries?
Why is public money spent on assets that don’t appear in public records?
In contexts like India, using GIS responsibly also means pushing for:
Open data policies
Better metadata and documentation
Regular updates
Public access to spatial information
But you don’t wait for these systems to be perfect before acting.
You work with what you have.
You show what is missing.
And you use that absence as leverage.
Not every analysis starts with clean data.
Some start with curiosity.
Some with frustration.
Some with a walk down a flooded street and a GPS point.
In Indian cities, GIS is often less about finding answers and more about making questions impossible to ignore.
And sometimes, using the data available is not just analysis—it is resistance.