Urban Form Dynamics and Modelling towards Sustainable Hinterland Development in North Cianjur, Jakarta–Bandung Mega-Urban Region

The urban form is the physical configuration of a city, developed over time and space. Urban form can be considered at different scales, from region to neighborhood, each carrying a different focus. North Cianjur serves as the hinterland and one of the conurbation corridors of the Jakarta–Bandung Mega-Urban Region, meaning that the balance between its function as an environmental buffer area and the destination of urban growth needs to be planned carefully. This paper explores the dynamics in North Cianjur and employs several model scenarios as a planning intervention using landscape dynamic tools and land-change modeling, with three scenarios employed: Business as Usual (BAU), Spatial Planning Policy (SPP), and Urban Containment (UCT). The result show that North Cianjur has transformed into a polycentric region with two urban zones, a peri-urban zone, and a rural zone in the northernmost part of the region. Urban form trends show a sprawling built-up pattern outside urban zones, and a compacted trend in urban zones due to expansion from the Jakarta and Bandung Metropolitan Area. UCT models appear to be the most optimal for implementation in North Cianjur, representing a way to accommodate urban growth and expansion inside the urban center while still maintaining regional sustainability.

A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling

Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future date requires the determination of an annualized matrix through eigendecomposition followed by matrix powering. However, the technique yields multiple solutions, commonly with imaginary parts and negative transitions, and possibly with no non-negative real stochastic matrix solution. In addition, the computational burden of the procedure makes it infeasible for practical use with large problems. This paper describes a Regression-Based Markov (RBM) approximation technique based on quadratic regression of individual transitions that is shown to always yield stochastic matrices, with very low error characteristics. Using land cover data for the 48 conterminous US states, median errors in probability for the five states with the highest rates of transition were found to be less than 0.00001 and the maximum error of 0.006 was of the same order of magnitude experienced by the commonly used compromise of forcing small negative transitions estimated by eigendecomposition to 0. Additionally, the technique can solve land change modeling problems of any size with extremely high computational efficiency.