[Paper Review] Measuring Primitive Accumulation: An Information-Theoretic Approach to Capitalist Enclosure in PIK2, Indonesia

Sandy Hardian Susanto Herho, Alfita Puspa Handayani+3 more|arXiv (Cornell University)|Mar 14, 2026

Land Use and Ecosystem Services0 citations

TL;DR

The paper introduces an information-geometric, Markov-chain, and percolation framework to quantify the rate, topology, and non-stationarity of capitalist enclosure in Indonesia’s PIK2 megadevelopment using eight years of Sentinel-2 LULC data.

ABSTRACT

Large-scale land enclosure for speculative mega-development constitutes a non-equilibrium spatial process whose velocity, topology, and irreversibility remain poorly quantified. We study the Pantai Indah Kapuk 2 (PIK2) coastal mega-development north of Jakarta, Indonesia, using eight years (2017--2024) of Sentinel-2 land-use/land-cover (LULC) data at 10-meter resolution. The landscape is projected onto a Marxian probability simplex partitioning terrestrial pixels into Commons, Agrarian, and Capital fractions. Fisher-Rao (FR) geodesic distances on this simplex identify a transformation pulse of $0.405$~rad/yr during 2019--2020, coinciding with major construction activity. Absorbing Markov chain analysis yields expected absorption times into the built environment of $46.0$~years for cropland and $38.1$~years for tree cover, with a pooled built-area self-retention rate of $96.4\%$. Percolation analysis reveals that a giant connected component containing $89$--$95\%$ of all built pixels persists at occupation probabilities $p \in [0.096, 0.162]$, far below the random percolation threshold $p_c \approx 0.593$, indicating planned rather than stochastic spatial growth. The box-counting fractal dimension of the urban boundary increases from $d_f = 1.316$ to $1.397$, consistent with increasingly irregular frontier expansion. These results suggest that information-geometric and statistical-mechanical tools can characterize the kinematic and topological signatures of capitalist spatial accumulation with quantitative precision.

Motivation & Objective

• Quantify the rate and direction of landscape transformation from commons/agrarian to capital within PIK2.
• Develop a framework combining information geometry, Markov chains, and percolation theory to diagnose enclosure dynamics.
• Characterize spatial topology and connectivity of the built environment as it expands.
• Provide a quantitative baseline to distinguish planned enclosure from stochastic growth.
• Assess socio-ecological implications of rapid capital-driven land conversion.

Proposed method

• Project high-resolution LULC data onto a 7-class simplex and compute the Marxian ternary aggregation (Commons, Agrarian, Capital).
• Use Fisher-Rao geodesic distance on the simplex to measure transformation velocity and identify transformation pulses.
• Model pixel-level dynamics as an absorbing Markov chain with Built Area as the absorbing state to obtain expected absorption times.
• Compute percolation metrics (occupation probability, giant component, fractal boundary) to assess spatial connectivity and morphology.
• Apply a G-test and Frobenius norm to assess temporal non-stationarity of transition dynamics.
• Use box-counting to estimate fractal dimension of the largest cluster boundary and analyze cluster-size distributions.

Experimental results

Research questions

• RQ1 What is the rate and velocity of land-use transformation from agrarian/commons toward capital in PIK2, and when do rapid changes occur?
• RQ2 How can information-geometry, Markov chains, and percolation theory together diagnose the kinematic and topological signatures of enclosure?
• RQ3 What is the temporal non-stationarity of land-transition dynamics, and how are shocks reflected in transition matrices?
• RQ4 How does the evolving built environment affect spatial connectivity and frontier morphology?
• RQ5 What are the socio-economic implications of observed land conversion patterns for smallholders and coastal ecosystems?

Key findings

• The transformation pulse peaks during 2019–2020 with FR distance d_FR = 0.405 rad/yr.
• Absorption times under the absorbing chain are: Crops 46.0 years, Bare Ground 38.4 years, Trees 38.1 years, Flooded Vegetation 64.7 years, Water 70.5 years, with Built Area effectively acting as the absorbing state.
• Pooled transition matrix shows strong self-retention for Water (P_WW = 0.960), Built Area (P_BB = 0.964), and Crops (P_CC = 0.779).
• The global occupation probability p(t) grows from 0.096 in 2017 to 0.162 in 2024, while the giant connected component contains 89–95% of built pixels at p in [0.096, 0.162].
• Fractal dimension of the largest cluster boundary increases from df = 1.316 (2017) to df = 1.397 (2024).
• Entropy shows a net rise (H from 0.844 to 1.089 nats) with non-significant MK trend (tau = 0.50, p = 0.108).