three data journey cases

experimental/data_journeys/README.md

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+Cases for the data journeys presentation/paper.
+
+Originally prepared by simonf@ for AAG 2026
+
+*   [How the "Missing Survey" Drives India's Demolition Crisis](india_slum_demolition_survey_absence.md)
+*   [Spain SIGPAC](spain_sigpac_error_correction.md)
+*   [Malawi: Africa RiskView "No Trigger" Crisis (2015/16)](malawi_arc_no_trigger.md)

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