three data journey cases

PiperOrigin-RevId: 877167308

Author: The Google Earth Engine Community Authors

experimental/data_journeys/india_slum_demolition_survey_absence.md

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+# The Cartography of Erasure: How the "Missing Survey" Drives India's Demolition Crisis
+
+In the rapidly urbanizing landscapes of Delhi and Mumbai, the access to rehabilitation after slum evictions has become contingent on a specific geospatial record: the government survey. The state procures satellite maps every three months to detect new construction for demolition — demonstrating high geospatial capacity for enforcement.
+
+Eligibility for rehabilitation depends on a different instrument entirely: a joint survey and documentary proof under the 2015 [rehabilitation policy](https://delhishelterboard.in/main/wp-content/uploads/2019/01/Relocation-Policy-2015.pdf). We see two data journeys on the same territory: one completes, the other one is blocked.
+
+Courts have [repeatedly](https://indiankanoon.org/doc/159570569/) [emphasised](https://indiankanoon.org/doc/39539866/) that a detailed survey prior to eviction and a rehabilitation plan are essential to due process; in practice, disputes often centre on whether a meaningful survey happened and who was counted.
+
+In 2023 alone, approximately **280,000 people** were forcibly [evicted](https://tribe.article-14.com/post/2-8-lakh-homes-demolished-in-delhi-in-2023-but-rehab-fails-as-bjp-aap-ignore-their-own-promises-court-orders-66442cca27158) in Delhi, the [highest](https://www.newsclick.in/more-half-million-people-5-lakh-evicted-india-2023-report) number in India according to the Housing and Land Rights Network (HLRN) . Yet, rehabilitation often hinges on two gatekeepers: (1) whether a cluster is treated as ‘protected/eligible’ under the policy framework (officially limited to 675 recognized *Jhuggi Jhopri* or JJ clusters) and (2) whether households meet the policy’s cut-off dates, possess correct documentation, and appear in a [joint survey](https://www.newslaundry.com/2025/06/30/delhis-demolition-drive-27000-displaced-from-9-acres-of-encroached-land). Enforcement tends to be well-instrumented (e.g., satellite monitoring aimed at spotting ‘new’ jhuggis), while rehabilitation turns on slower, contested steps—joint surveys, voter-list appearance, and pre-2015 documents—where exclusions are common.
+
+A June 2025 [ground report](https://www.newslaundry.com/2025/06/30/delhis-demolition-drive-27000-displaced-from-9-acres-of-encroached-land) assessed 27,000+ people displaced across multiple demolition drives since the BJP came to power; it also reports that 344 homes (about five acres) were razed at Bhoomiheen Camp on June 11. In Mumbai, despite a slum population exceeding 5 million (42% of the city), Wilson Center reporting [estimates](https://www.wilsoncenter.org/article/building-slum-free-mumbai) about 0.15 million tenements rehabilitated under the model over two decades (with ~0.12 million approved but not begun), against an early target of 1 million.
+
+## What the State Could See — and What It Chose Not to Count
+
+### Selective Mapping
+The state demonstrates high-resolution precision when mapping land for infrastructure projects—metro lines, highways, and commercial complexes. This "structure mapping" facilitates clearance. Conversely, "social mapping"—the enumeration of families, tenure history, and eligibility—is systematically neglected.
+
+The landmark *Sudama Singh* [judgment](https://indiankanoon.org/doc/39539866/) (2010) of the Delhi High Court mandated that *prior* to any eviction, the state must conduct a survey of all persons to determine eligibility. In practice, agencies circumvent this by declaring settlements as "new encroachments" not found on the official list, thereby bypassing the obligation to survey or rehabilitate.
+
+### The Survey Gap: 6,343 vs. 675
+The magnitude of the data deficit is stark. The National Sample Survey Office (NSSO) 69th Round (2012) [identified](https://des.delhi.gov.in/sites/default/files/urban_slums_in_delhi.pdf) **6,343 slums** in Delhi, housing over a million households. However, the Delhi Urban Shelter Improvement Board (DUSIB) officially recognizes only **675 JJ Bastis** (clusters) for rehabilitation purposes. These figures aren’t perfectly like-for-like (NSS uses a broad ‘slum’ definition; ‘JJ clusters’ are an administrative category), but the comparison still illustrates a major gap between the estimated universe of informal settlements and the subset treated as protected/eligible in practice.
+
+This discrepancy means that nearly 90% of Delhi's slum settlements exist in a legal blind spot. Residents of these "non-notified" slums are vulnerable to summary eviction because their settlements do not appear on the JJ Bastis list used by policymakers. Slum coverage is [poorly organized](https://timesofindia.indiatimes.com/city/delhi/ud-department-seeks-information-on-82-slum-clusters-gone-missing-in-delhi/articleshow/99222772.cms), with documents often unavailable.
+
+
+## How Absence Became Ineligibility
+
+### The "Cut-Off" Date Trap
+Eligibility for rehabilitation is anchored to arbitrary chronological milestones. In Delhi, the *Delhi Slum & JJ Rehabilitation and Relocation Policy, 2015* stipulates a dual requirement: the slum cluster must have existed prior to **January 1, 2006**, and the individual family must possess documents proving residence prior to **January 1, 2015**.
+
+In Mumbai, the exclusionary mechanics are even more stratified. Residents are entitled to free housing only if they can prove residence prior to **January 1, 2000**. Those arriving between 2000 and 2011 fall into a "paid rehabilitation" tier, requiring them to pay [₹2.5 lakh](https://citizenmatters.in/no-more-free-rehousing-but-government-offers-discounted-homes-to-slum-dwellers/) (with some early proposals pushing even higher) for a replacement tenement—a sum often prohibitive for the urban poor. Structures outside those windows are ineligible.
+
+The state's failure to update these maps creates a "relocation gap." Families displaced from surveyed areas often move to unsurveyed peripheral lands, where they are again classified as "new encroachers," [trapping](https://www.wilsoncenter.org/article/building-slum-free-mumbai) them in a cycle of perpetual displacement.
+
+### Judicial Ambivalence
+The judiciary, once the guardian of housing rights, has increasingly [facilitated](https://article-14.com/post/how-delhi-s-govt-is-using-its-slum-rehabilitation-policy-to-deny-rehabilitation-to-those-who-need-it-6858cb6129469) this erasure. While *Sudama Singh* (2010) and *Ajay Maken* (2019) established robust protections, recent rulings have diluted them. A 2022 Delhi High Court [ruling](https://indiankanoon.org/doc/131330551/) interpreted the 2015 Policy restrictively, effectively [denying](https://scroll.in/article/1053522/how-a-delhi-high-court-judgement-has-made-slum-residents-vulnerable-to-arbitrary-evictions) relief to residents of non-notified slums because they were not on the DUSIB list.
+
+This jurisprudence effectively treats the "list" as conclusive proof of existence, ignoring the reality that the list itself is outdated and incomplete. The courts now frequently treat the absence of a survey as the resident's failure rather than the state's breach of duty.
+
+
+
+### The Right to be Counted
+The "geospatial void" is a political choice. Slum dweller organizations argue that the refusal to survey is a refusal to recognize citizenship. Residents are currently [demanding](https://cpiml.org/mlupdate/slum-dwellers-convention-demands-permanent-halt-on-bulldozer-rampage-in-delhi) a fresh survey of all JJ clusters, with a revised cut-off date of **July 1, 2025**, to reflect the reality of post-2015 urbanization.
+
+Without a survey, a family that has lived in a *jhuggi* for twenty years—paying for electricity and voting in elections—is legally indistinguishable from a squatter who arrived yesterday.
+
+## See also
+[Policy Analyses and Recommendations for the
+Delhi Urban Shelter Improvement Board](https://spia.princeton.edu/sites/default/files/content/India_Policy_Workshop_Report-Final.pdf) from the Woodrow Wilson School
+
+## Framework annotation
+
+* blockade->void: enforcement data journey completes while recognition data journey is strategically blocked; governing effect is non-existence — families with decades of residence rendered invisible
+* the cut-off dates and list requirements launder the void through gate-like procedural language ("no survey, no list, no entitlement")
+* courts in Sudama Singh and Ajay Maken were reaching for something like a warrant card — demanding the state declare what it knows before it demolishes.

experimental/data_journeys/malawi_arc_no_trigger.md

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+# Malawi: Africa RiskView "No Trigger" Crisis (2015/16)
+
+In the 2015/16 agricultural season, Malawi declared a national state of disaster as drought left 6.5 to 6.7 million people [food-insecure](https://actionaid.org/publications/2017/wrong-model-resilience). The government had purchased drought insurance costing approximately $4.7 million (often rounded to $5 million) from the African Risk Capacity (ARC), a G7-backed parametric insurance mechanism. ARC is designed to provide rapid payouts —typically within [2-4 weeks](https://www.arc.int/sites/default/files/2021-09/XCF-Policy-Brief-Summary.pdf))— when satellite-detected rainfall deficits lead to estimated response costs [exceeding](https://www.arc.int/how-arc-works) specified thresholds.
+
+ The insurance did not pay out immediately. The satellite model [calculated](https://www.researchgate.net/publication/322357484_The_wrong_model_for_resilience_How_G7-backed_drought_insurance_failed_Malawi_and_what_we_must_learn_from_it) that only **20,594** people had been affected—a figure over three hundred times lower than reality. The model had been calibrated for a crop that farmers no longer grew.
+
+## What the Index Measured — and What It Missed
+
+### The Water Requirements Satisfaction Index
+Africa RiskView, ARC's technical engine, uses the Water Requirements Satisfaction Index (WRSI)—a [model](https://www.arc.int/drought) developed by the FAO in the 1970s that monitors water deficits throughout the growing season. The model takes satellite-based rainfall estimates and [calculates](https://africariskview.org/Content/Technical-Note_en.pdf) the ratio of actual to potential evapotranspiration: how much water the crop received versus how much it needed.
+
+WRSI translates rainfall into crop stress, and crop stress into estimated affected populations. When the index crosses a predetermined threshold, insurance payouts are triggered automatically—no claims process, no ground verification, no delay. This is the promise of parametric insurance: speed at scale.
+
+But WRSI requires accurate parameterization. The model must know what crop is being grown, when it was planted, how long its growing season lasts, and what its water requirements are at each growth stage. Get these parameters wrong, and the model monitors a phantom crop while the real one dies.
+
+### The Phenology Mismatch
+Malawi's ARC policy was [calibrated](https://www.arc.int/sites/default/files/2021-10/Malawi-Response-Matrix-Clean.pdf) assuming farmers primarily planted late-maturing maize varieties with 120-140 day growing cycles. This assumption was embedded in the model's "crop calendar"—the temporal window during which rainfall deficits would be monitored.
+
+But Malawian farmers, responding to shifting climate patterns and extension advice about drought risk, had increasingly switched to early-maturing varieties with 90-day cycles. These shorter-season crops mature faster, requiring rain at different times than their longer-season cousins.
+
+The 2015/16 rainfall pattern was particularly cruel to short-cycle maize. Dry spells hit precisely during the critical growth phases of the 90-day varieties. But the model, looking for deficits in the 120-140 day window, recorded a "passable" season. The long-cycle maize the model tracked would have survived; the short-cycle maize that farmers actually planted did not.
+
+### The Trigger That Didn't Trigger
+When rainfall becomes low enough, the WRSI value drops. If this agronomic index falls below a critical threshold during the monitored window, the model estimates a high affected population and a correspondingly high response cost, triggering the insurance. In 2015/16, Malawi's WRSI—calculated for the wrong crop—stayed above this critical threshold. The model said: minor drought, 20,594 affected. The government said: national emergency, 6.7 million affected.
+
+## How the Proxy Became the Decision
+
+### The $4.7 Million Premium, $300 Million Gap
+Malawi paid ~$4.7 million in insurance premiums for coverage that was supposed to address drought. The eventual $8.1 million payout—itself contested as inadequate—arrived against a backdrop of a humanitarian funding gap estimated at over $300 million (total response costs were ~$395 million). ActionAid's analysis concluded the payment was "too little, too late" and "effectively represented an economic loss to Malawi".
+
+The arithmetic was brutal: Millions paid for insurance that didn't trigger when needed, forcing the government to seek emergency humanitarian funding anyway, then receiving a small payout nine months later that couldn't address the immediate crisis it was meant to prevent.
+
+For ActionAid and local civil society, the failure highlighted a severe opportunity cost: the $4.7 million premium could have funded decentralized weather stations, irrigation, or the Village Savings and Loans (VSLs) networks that rural Malawian women actually relied on during crises. Government officials indicated they would not renew the policy.
+
+### The Epistemological Problem
+Parametric insurance depends on the premise that satellite observations can substitute for ground truth — that the model's assessment is close enough to reality that automated triggers are preferable to slow, expensive claims verification. The Malawi case illustrates what happens when that premise fails.
+
+The problem wasn't that satellites can't measure rainfall accurately. The problem was that the chain from rainfall to crop stress to food insecurity requires knowledge that satellites cannot provide: what farmers actually planted, when they planted it, and how their specific varieties respond to specific weather patterns. This "ground truth" about cropping patterns was outdated, and the model had no mechanism to detect its own obsolescence.
+
+### The Retrospective Recalculation
+In its own defense, ARC maintained that the system's safeguards functioned as intended. When discrepancies emerged between the model's output and the ground truth reported by the Malawi Vulnerability Assessment Committee (MVAC), ARC's framework allowed for a technical review. After months of dispute and field investigations, ARC recalculated the model using the corrected 90-day maize parameters. The revised index showed severe drought impact—triggering the payout.
+
+In November 2016, ARC announced an $8.1 million payout. By January 2017, when the funds finally arrived, the humanitarian response had been funded through other means, the planting season had passed, and the "rapid" in "rapid response" had failed to materialize. What was designed as 2-4 week emergency financing arrived nine months after the disaster declaration.
+
+
+### The Legitimacy Collapse
+The Malawian government formally disputed ARC's "satellite truth," forcing an expensive retrospective ground audit that ARC's model was designed to avoid. Kenya and Malawi [pulled out](https://www.globalinclusiveinsuranceforum.org/sites/default/files/The-Future-of-Disaster-Risk-Pooling-for-Developing-Countries.pdf) of ARC for the 2016-17 season. Participation across the continent plummeted: just three countries (Burkina Faso, Senegal, The Gambia) took part in the 2018/19 pool. The model's failure in one country contaminated confidence across the continent.
+
+## The Aftermath: Evolution and Return
+
+The 2015/16 crisis was [studied](https://www.researchgate.net/publication/322357484_The_wrong_model_for_resilience_How_G7-backed_drought_insurance_failed_Malawi_and_what_we_must_learn_from_it) and [catalyzed](https://www.arc.int/sites/default/files/2021-10/ARC_LessonsLearned.pdf) significant reforms:  ARC instituted mandatory customization reviews, sensitivity analyses, and strengthened in-country Technical Working Groups. Malawi eventually rejoined the risk pool, and during the 2021/2022 season the updated system successfully triggered a $14.2 million payout — proving the model could learn.
+
+## Framework annotation
+
+* path->gate: linear travel from satellite to model to reinsurer to trigger; binary threshold determination
+* asymmetric uncertainty burden: model permitted to guess at national scale; government required to prove it wrong at field level, retroactively, at own expense
+* this sits on the diagonal