Friday, July 18, 2025

Vegetation Loss and Gain in Kochi Region (2020–2025): A Remote Sensing Study

 

Vegetation changes detection plays a critical role in understanding urban expansion, ecological degradation, and climate resilience. Here I explore the Normalized Difference Vegetation Index (NDVI) for Ernakulam District, Kerala, using Sentinel-2 satellite imagery for the years 2020 and 2025. The objective is to detect areas of vegetation gain, loss, or stability, and to generate clear visual and quantitative indicators to support future environmental planning.

Data Sources

  1. Satellite Imagery: Sentinel-2 Level-2A imagery (10 m resolution) from Copernicus Open Access Hub @ 10% cloud cover.
  2. Bands Used:
    1. Band 8 (NIR - Near Infrared)
    2. Band 4 (Red)

Methodology

  1. NDVI Computation NDVI was calculated for both 2020 and 2025 using the formula:

NDVI =

Computation was done in QGIS using the Raster Calculator.

  1. Clipping and Masking NDVI rasters were clipped using the Ernakulam district mask to focus the analysis within administrative boundaries.
  2. NODATA Handling Areas without data (e.g., outside district boundary or cloud cover) were assigned a NODATA value (-9999) using the 'Build Virtual Raster (VRT)' tool with masking.
  3. NDVI Rendering Visual interpretation was improved by applying a pseudocolor ramp:

NDVI < 0: built-up or water (Red)

0.0–0.2: sparse vegetation (Yellow)

0.2–0.4: moderate vegetation (Light Green)

0.4: dense vegetation (Dark Green)

  1. Change Detection A raster layer showing difference between 2025 and 2020 was created:

NDVIchange = NDVI2025 – NDVI2020

  1. Threshold Classification NDVI changes were categorized:

Gain: > +0.05

Loss: < -0.05

Binary masks were generated for each category.

  1. Water Body Isolation Pixels with very low NDVI were mapped to highlight major water bodies (e.g., Lakes, streams, dam storage) using a custom mask.

Visual Outputs:

Figure 1: NDVI condition in 2020; The NDVI analysis for May 2020 uses data from a single Sentinel-2 tile (T43PFM), covering the majority of Ernakulam district. Peripheral slivers that fell outside the tile were not included, as their omission was not expected to affect overall spatial trends or comparative patterns with 2025 data



Figure 2: NDVI condition in 2025

Figure 3: Changes: Composite map showing greening (blue-green), loss (magenta), and stable zones (neutral green)


These visuals help observe spatial patterns: urban fringe vegetation loss, greening in north-eastern hills, and stable agricultural areas.

This NDVI-based approach reveals useful trends in greenness. With additional zoning, socio-economic, or temperature overlays, this framework can evolve into a powerful climate-resilience diagnostic for Kerala districts.


LST map of Kerala District-wise (by the Author)





Friday, July 11, 2025

Energy Use: A Reflection in Stillness

Some of us live in places where the tap flows all day. Some walk a few miles for a bucket of water. Some of us cool our rooms to precise degrees. Others open windows, shift the curtain, wait for the breeze. We light up cities through the night, move through time zones with the ease of a boarding pass. Elsewhere, dusk still marks a slowing down. There is no one way to live. But each way leaves a trace.
I have always believed that growth is not the opposite of restraint. One can walk forward with care. Build with thought. It’s not always about doing less—but doing with awareness. The world is full of clever systems, and many of them serve us well. But sometimes, what’s efficient isn’t what’s kind. To the earth. Or to each other.
When I look at the world from above, it hums with activity. But look closer, and it’s uneven. Energy flows more freely in some directions. Air warms more quickly in some corners. And some voices carry farther in the room. We call this a global conversation. But are we all speaking the same language?
This isn’t a reckoning. It’s a pause. A moment to consider that maybe the future we are building needs not only new technology, but new humility. To know that just because something is possible doesn’t mean it is owed. To realize that comfort is not a measure of wisdom. And that the planet, patient as it has been, is listening more than we think.
There is no villain here. No single hero either. Only a field of footsteps, moving at different paces. We walk through weather shaped by others, and we shape the sky for those who come after. That’s all.

Friday, April 18, 2025

Rethinking Participation in Urban Governance: Kerala at the Crossroads

Is participation real or ritualistic?

In Kerala, the answer is not straightforward.

The decentralisation experiment, with its celebrated Grama Sabhas and three-tier governance structure, has earned global admiration. But does this machinery deliver genuine democratic participation—or does it merely simulate it? Let us explore that question by revisiting global theories of participation, mapping Kerala’s practices on those scales, and imagining bold futures powered by data, sensors, and civic intelligence.

At the heart of the debate lies a deeper inquiry: what does it mean to “participate” in governance? Is it merely the right to be informed or consulted, or does it mean shaping budgets, influencing decisions, holding the system accountable—and being held accountable in turn?

The Ladder and the Labyrinth

In 1969, Sherry Arnstein’s Ladder of Citizen Participation marked a foundational moment in how the world would come to evaluate public involvement in governance. Arnstein’s eight rungs begin with manipulation and therapy—forms of participation in name only—then ascend through informing, consultation, and placation, before finally reaching partnership, delegated power, and citizen control.

"Arnstein's ladder" of citizen participation. Source: https://en.wikipedia.org/wiki/Sherry_Arnstein 

Fifty years later, the ladder has grown into multidimensional frameworks like the Participation Cube, which allow us to consider not just the depth of participation, but also its breadth, frequency, and inclusivity. These models urge us to ask: participation by whom, for what, how often, and with what consequence?

Placed against this conceptual backdrop, Kerala occupies a complex and shifting position. The architecture of decentralisation—the Panchayati Raj institutions, the People's Planning Campaign, the Grama Sabhas, the rights-based approach to development—is really ambitious. The statutory structure invites participation not just in isolated events but as a systemic right, embedded within planning, budgeting, and monitoring processes.

And yet, as any local resident will admit, these Sabhas often becomes an event to attend, not a forum to influence. Meetings are called, resolutions passed, reports read out. But the presence of people does not always mean the presence of, or expression of power.

Between Design and Delivery

To evaluate the state of participation in Kerala today is to distinguish between designed participation and delivered participation. On paper, Kerala performs spectacularly. It mandates periodic Grama Sabhas, ensures representation through quotas, provides institutional platforms like working groups and development seminars, and allocates substantial budgetary powers to local bodies.

But in practice, participation is often reduced to consultation. A 2023 audit of ward-level Sabha attendance in multiple districts showed that turnout was less than 10% in urban panchayats and below 15% even in well-functioning rural areas. Women and marginalised communities were present, but rarely vocal. Youth turnout was almost negligible. Decisions, when made, tended to ratify proposals prepared in advance by officials or intermediaries.

Why does this gap persist? Partly because structures alone do not ensure agency. Legal architecture can invite participation, but the culture of governance must also nurture it. Bureaucratic capture, political tokenism, and citizen fatigue often combine to dilute what was once a radical vision.

From Participation to Power

To move forward, we must ask: how can Kerala make citizen participation more meaningful, legitimate, and transformative?

A useful starting point is to recognise participation as more than attendance. Participation is about agency—the capacity to shape, to contest, to co-create. It is also about continuity, not just episodic engagement. And most importantly, it is about consequence. If participation does not change decisions, it ceases to matter.

Kerala could lead the way by developing a Participation Index—a composite measure that tracks not just how many meetings were held, but who spoke, what proposals were accepted, which projects emerged from citizen input, and how many were eventually implemented. This could be visualised ward-wise, published publicly, and tracked year on year. Just as health outcomes are measured through indicators, so too can democratic health be assessed—systematically and honestly.

Would such data make a difference? Yes—if it is used not to blame, but to learn. Participation fails when it is evaluated through anecdote. It can be revived when it is evaluated through shared reflection.

Learning from Elsewhere: Time-Tested Practices in Public Participation

Let us first ask—what have democracies around the world already learned about effective, inclusive, and sustained public participation? And, there is no shortage of inspiration.

Across continents, local governments have developed low-tech, high-trust mechanisms that embed citizen voices meaningfully into decision-making. These are not always dramatic innovations; more often, they are systems that work quietly, consistently, and transparently. Here are a few worth considering:

1. Participatory Budgeting (PB) – Porto Alegre to Paris

Arguably the most widely referenced model of deep participation, Participatory Budgeting began in Porto Alegre, Brazil, in the late 1980s. It allowed ordinary citizens—not experts or politicians—to decide how a portion of the municipal budget would be spent, through open deliberations at the neighborhood level.

Since then, cities from New York to Paris have adopted PB, modifying it to suit scale and context. In Paris, for example, residents can propose and vote on projects citywide using simplified online platforms. What matters is not the method, but the mindset: trusting people to make real fiscal decisions, and building institutional processes to support that.

Kerala’s People’s Plan once mirrored this spirit. It is time we revisit those roots and go further.

2. Standing Citizens' Panels – Scotland, Canada, Australia

In cities like Melbourne and Edinburgh, permanent citizens' panels—demographically representative groups selected through sortition (the action of selecting or determining something by the casting or drawing of lots) —serve as ongoing advisory bodies to city councils. These panels meet regularly, review policies, scrutinize service delivery, and offer long-term insights.

Unlike Grama Sabhas that meet sporadically and often suffer from low attendance, these panels create a culture of sustained dialogue. Because members are selected to reflect social diversity, and because their role is formalised, they bring both legitimacy and continuity to participation.

3. Civic Lotteries and Deliberative Assemblies – Ireland, France

When Ireland grappled with sensitive issues like same-sex marriage or abortion rights, it turned not to experts but to ordinary citizens. Through civic lotteries, a diverse group of people was selected and then immersed in learning, deliberating, and debating over months before submitting recommendations to Parliament.

France used a similar model to inform national climate policy. These examples show that deliberative participation can operate even at national levels—and that informed citizens, when given time and trust, can offer reasoned, equitable perspectives.

Could Kerala initiate a district-level citizen assembly each year to deliberate on one key development challenge?

4. Embedded Participation in Service Loops – Helsinki, Seoul

Not all participation happens in town halls. Some of the most effective practices are embedded into daily service interactions.

In Helsinki, public transport users can flag route inefficiencies through SMS codes at bus stops. In Seoul, school management committees involve parents and local residents in curricular and facility-related decisions, and such participation is tied to funding benchmarks.

What these approaches offer is distributed participation—not as a grand event, but as a thousand small moments of voice. Kerala’s Kudumbashree network, anganwadis, school PTAs, and MGNREGS job cards all offer entry points to embed civic feedback loops.

5. Community-Run Data Audits – India’s Own MKSS and NREGA Samvads

India itself has been a pioneer in community-led transparency. The MKSS movement in Rajasthan first introduced the concept of Jan Sunwais—public hearings where government expenditures were read out and questioned by villagers. The practice later influenced national rights-based laws like RTI and NREGA.

In states like Jharkhand and Chhattisgarh, civil society has used NREGA Samvads—local dialogues that audit employment records and payments using printed job cards and muster rolls—to hold local officials accountable. These are tools of voice, dignity, and data.

Why not revive such formats in Kerala—not just for wages, but for public works, welfare delivery, and municipal services? We are certainly more digitally savvy, so that could be the route?

Digital Frontiers and Sensorial Futures

The future of participation also lies in reimagining how we listen to citizens. Must every opinion be spoken in a Sabha hall or scrawled on a form? Or can it be sensed, measured, and aggregated in other ways?

Imagine a Kerala where smart phones capture not just mobility patterns, but civic priorities: footpath congestion in a ward, waiting times at public toilets, missed garbage pickups, or even heat stress reported through wearables. These are signals of participation too—signals of what people need, experience, and endure. When aggregated ethically, and visualised clearly, such data can guide more responsive governance.

Similarly, interactive dashboards could allow citizens to track ward budgets, rate local amenities, or even upload issues. Such platforms already exist in cities like Taipei and Barcelona. Kerala—with its digital literacy and dense institutional networks—is well placed to pioneer this model in the Global South.

This is not to argue for technology in place of human engagement, but rather for a hybrid model: sensor data where relevant, deliberation where needed, and co-governance where possible.

The Way Forward: A Culture, Not a Campaign

Kerala’s participatory journey must now evolve from being a campaign to becoming a culture. Campaigns mobilise. Culture sustains. The real success of decentralisation lies not in one-off innovations but in building institutional reflexes—where administrators expect to co-design with citizens, and citizens expect to be heard and respected, not merely informed.

We must reclaim the political in participation—not in a partisan sense, but in the deepest sense of that word: as a collective negotiation of the public good.

In that pursuit, Kerala is not starting from scratch. It has already walked further than most. The challenge is not invention, but reinvention. Not just more meetings, but better meanings. Not just new platforms, but renewed trust.


Monday, March 17, 2025

Unraveling Housing Growth in Kerala: An Economic and Infrastructure Perspective

 

Housing construction is a critical indicator of economic vitality and urban expansion. In Kerala, where land availability is constrained by geography, demography, and policy interventions, understanding the factors driving new residential development is essential. Unlike rapidly expanding metropolitan regions, Kerala’s housing growth follows a unique trajectory shaped by economic activity, infrastructure development, and government-led interventions such as the LIFE Mission and PMAY (Urban & Rural) schemes.

This study explores the economic and spatial determinants of housing growth across Kerala’s districts. A closer look at district-wise economic activity reveals that the stretch from Ernakulam to Kannur exhibits a distinctive pattern—higher economic productivity, greater infrastructure development, and an active housing market. Whether this signals the emergence of a well-connected economic corridor or reflects broader state-wide trends is a key question guiding this analysis.

Additionally, Kerala’s housing expansion poses a crucial dilemma:

  • Is growth occurring through urban densification, where existing cities accommodate more residents?
  • Or is housing growth primarily driven by outward expansion into previously undeveloped or ecologically sensitive areas?

This distinction holds significant implications for environmental sustainability, infrastructure planning, and equitable growth. If housing development follows an outward sprawl pattern, it could lead to fragmented urbanization and the risk of encroaching upon Kerala’s fragile ecosystems. On the other hand, if densification is driving growth, cities may need better policies to accommodate rising populations while ensuring livability.

To explore these dynamics, this study integrates multiple datasets and analytical techniques, including spatial mapping, economic indicators, and Ordinary Least Squares (OLS) regression models. The analysis examines both economic forces (business activity, infrastructure, per capita income) and urban mobility indicators (motor vehicles, population density, tourism) to determine their respective roles in housing growth.

Before diving into econometric analysis, the following sections will establish a district-level spatial and economic overview, setting the stage for a deeper investigation into the key drivers of Kerala’s housing expansion.


Percentage of Urbanisation: GIS map by me. Date source: KERALA Economic Review_2024_Eng_Vol 1


Regional Economic Trends: GDVA at Basic Prices (₹ in Lakh) Across Kerala's Districts. Graph by me. Date source: KERALA Economic Review_2024_Eng_Vol 1


The chart reveals a noticeable economic corridor of high productivity extending from Ernakulam to Kannur, encompassing Thrissur, Palakkad, and Kozhikode. These contiguous districts exhibit relatively high GDVA, indicating strong commercial, industrial, and service sector activity. This region benefits from well-developed infrastructure, transportation networks, and urbanization, making it a key driver of Kerala’s economy. In contrast, districts like Idukki and Wayanad, with their predominantly forested and hilly terrain, show lower economic output, reinforcing the role of geography in shaping economic performance. Understanding these spatial patterns helps contextualize regional development strategies and housing growth dynamics.

Exploring the Link Between Population Density and Per Capita Income

Before delving into our regression analysis, we first examine a fundamental relationship in urban economics: the correlation between population density and per capita income. Understanding this link is crucial, as density can often indicate the degree of economic activity, access to infrastructure, and urbanization levels.

Is there a Correlation between Population Density & Per Capita Income? Graph by me. Date source: KERALA Economic Review_2024_Eng_Vol 1

Two different measures of correlation provide insight into this relationship:

  • Pearson correlation coefficient: 0.42 – This suggests a moderate positive correlation between population density and per capita income. This implies that, generally, districts with higher population density tend to have higher per capita income, though the relationship is not particularly strong.
  • Spearman correlation coefficient: 0.38 – A slightly weaker correlation, indicating that while there is a positive trend, the rank order of districts by density does not perfectly match their rank order by per capita income.

Scatter plot by me. Date source: KERALA Economic Review_2024_Eng_Vol 1


The scatter plot further illustrates this relationship, with the red trendline showing a positive slope, confirming that districts with higher population densities do tend to have higher per capita incomes. However, the spread of data points indicates that other factors are at play, and population density alone does not fully explain income variations.

This correlation serves as a starting point for our deeper analysis. While a moderate relationship exists, the next step is to examine multiple economic and infrastructural factors together to understand what truly drives housing development and economic growth across Kerala's districts.

Mapping Kerala's Housing Landscape

Before delving into our econometric analysis, it is crucial to visualize the spatial distribution of key factors that influence housing development across Kerala. The following maps and charts provide a clearer picture of the regional variations in housing initiatives, land use, and infrastructure growth.

  1. Housing Development Through Life Mission:

The Life Mission initiative has played a significant role in addressing housing shortages, with varying levels of success across districts. The first chart showcases the number of houses built in each phase, highlighting the concentration of housing efforts in districts such as Thiruvananthapuram, Palakkad, and Malappuram.

1.      Extent of Homelessness Addressed:


The second graph illustrates the number of homeless and landless families yet to be rehabilitated.

 


1.      Forest Cover and Land Constraints:

 




The third visualization shows the distribution of forest cover across Kerala. Districts such as Idukki, Wayanad, and Palakkad possess large tracts of ecologically sensitive land, which inherently limits urban expansion. The interaction between land availability and housing growth is a key factor in determining where new construction can feasibly occur.

These visual insights set the stage for a deeper quantitative analysis. These spatial trends highlight the interplay between economic activity, government intervention, and geographic constraints. While Life Mission has made significant contributions, forested districts like Wayanad and Idukki face inherent limitations to expansion. Meanwhile, high economic output in urbanized regions suggests infrastructure and economic vibrancy as key drivers of new housing construction. The following econometric models test whether business activity, infrastructure, mobility, or tourism best explain housing growth patterns across Kerala’s districts.

Two Key Drivers of Housing Growth: Economic Expansion and Urban Mobility.

Two separate OLS regression models were developed to investigate housing growth.

  1. The first model explores the relationship between housing construction and economic/infrastructure indicators, focusing on:
    • Per Capita Income (Economic well-being and housing affordability)
    • Number of Registered MSMEs (A proxy for business growth and employment)
    • Length of PWD Roads (Infrastructure accessibility and urban expansion)
      Graph by me. Date source: KERALA Economic Review_2024_Eng_Vol 1

  1. The second model shifts focus to urbanization and mobility factors:
    • Percentage of Urban Population (Extent of urbanization)
    • Population Density (Indicator of land availability and demand constraints)
    • Number of Motor Vehicles (Economic mobility and urban demand)
    • Domestic Tourist Arrivals (Potential impact of tourism-induced housing demand)

By analyzing these two models side by side, we derive a broader understanding of Kerala’s housing growth patterns and the potential policy implications.

 

Regression Results and Interpretation

Model 1: Economic and Infrastructure Drivers of Housing Construction

This model examines the influence of economic and infrastructural factors—Per Capita Income, MSME presence, and Road Length—on the number of new residential units.

 

Dependent variable:

`Total_Residential_Units_21-22`

Per_Capita_Income

-0.062***

(0.011)

Number_of_Registered_MSMEs

2.380***

(0.152)

Length_of_PWD_Roads_km

1.608*

(0.816)

Constant

5,354.697**

(1,736.952)

Observations

14

R2

0.970

Adjusted R2

0.961

Residual Std. Error

1,334.727 (df = 10)

F Statistic

108.910*** (df = 3; 10)

Note:

*p**p***p<0.01


Findings:

Variable

Effect on New Housing Units

Reliability

Per Capita Income

For every ₹1,000 increase in per capita income, new housing decreases by 62 units

Highly reliable (p < 0.01)

Number of Registered MSMEs

For every 100 new MSMEs, 238 more houses are built

Very strong effect (p < 0.001)

Adjusted R²

96.1% of housing variation explained by this model

Very strong model

F-statistic

Model overall is statistically significant (p < 0.01)

 

  • Economic activity (MSMEs) is a major driver of housing growth, likely due to increased employment and migration towards business hubs.
  • Per capita income negatively correlates with new housing construction, suggesting that wealthier districts either already have sufficient housing stock or rely more on redevelopment than new construction.
  • Infrastructure expansion (road networks) shows a moderate positive effect on housing, supporting the idea that accessibility boosts residential growth.

 

Model 2: Urbanization and Mobility Factors in Housing Growth

This model explores the impact of urbanization rate, population density, motor vehicle ownership, and domestic tourism on housing growth.

 

Dependent variable:

`Total_Residential_Units_21-22`

percentage_urban_pop

5,014.516

(5,982.209)

Density_per_sqkm

-6.435

(3.738)

Number_of_Motor_Vehicles_L

1,294.811***

(246.772)

Domestic_Tourist_Arrivals_2023_L

-228.366***

(65.564)

Constant

6,739.679**

(2,074.591)

Observations

14

R2

0.882

Adjusted R2

0.829

Residual Std. Error

2,806.003 (df = 9)

F Statistic

16.797*** (df = 4; 9)

Note:

*p**p***p<0.01

 

While economic growth indicators like MSME presence and road expansion significantly drive housing development, urban mobility trends add another layer of insight. The number of motor vehicles shows a strong correlation with housing expansion, reinforcing the idea that housing growth follows economic dynamism. However, traditional urbanization metrics—such as population density—do not show a strong effect, suggesting that housing growth is expanding outward rather than concentrating in existing urban centers.

Findings:

Variable

Effect on New Housing Units

Reliability

Percentage of Urban Population

A 10% increase in urbanization adds 500 new houses, but this effect is statistically weak

Unreliable (p > 0.10)

Number of Motor Vehicles

For every 1,000 new vehicles, 1,294 more houses are built

Very strong effect (p < 0.01)

Domestic Tourist Arrivals

For every 10,000 additional tourists, 228 fewer houses are built

Highly reliable (p < 0.01)

Adjusted R²

82.9% of housing variation explained by this model

Strong model

F-statistic

Model overall is statistically significant (p < 0.01)

 

 

  • More motor vehicles correlate with higher housing construction, reinforcing the role of economic activity and mobility in residential expansion.
  • Urbanization percentage and density are weak predictors, suggesting that new housing is expanding outward rather than densifying existing urban areas.
  • Domestic tourism negatively correlates with housing growth, indicating that tourism-driven demand might be concentrated in rental accommodations rather than new residential projects.

 

Key Takeaways: What Do These Findings Tell Us?

  1. Economic growth, not just population growth, fuels housing expansion

MSME density and vehicle ownership are more predictive of housing demand than population density or overall urbanization levels.

  1. Urban sprawl rather than densification

Higher per capita income areas are not driving new housing, and urban percentage does not significantly predict construction. This suggests that growth is expanding outward into newly developing peri-urban areas, rather than densifying existing city centers.

  1. Tourism is not a major driver of new residential construction

Despite Kerala’s prominence as a tourist destination, the housing sector does not appear to be directly influenced by tourism. This could mean that much of the tourism-related housing demand is met through hotels and homestays rather than new residential construction.

 

Conclusion

By integrating both analyses, this study provides a holistic perspective on Kerala’s housing growth dynamics. The findings indicate that economic activity and infrastructure development play a more significant role in housing construction than mere population growth. Notably, the expansion of roads and business activity correlates with increased housing units, suggesting that new developments are more aligned with outward expansion rather than densification. This pattern highlights the importance of strategic infrastructure planning to guide urban growth while mitigating risks of urban sprawl.

These insights are crucial for policymakers seeking to balance economic expansion with sustainable urban planning. A more nuanced approach—considering both economic drivers and spatial constraints—can help optimize housing policies to enhance connectivity, preserve ecological integrity, and ensure equitable growth.

This study not only reveals the economic and spatial determinants of Kerala’s housing expansion but also highlights the urgency of sustainable urban planning. If policymakers fail to guide housing growth strategically, rapid expansion could strain infrastructure and disrupt ecological balance. Future work integrating GIS-based spatial analysis will provide more precise, data-driven interventions to ensure that Kerala's housing boom aligns with both economic growth and environmental sustainability.