  Land use land cover changes (LULCC) are generally modeled using multi-scale
spatio-temporal variables. Recently, Markov Chain (MC) has been used to model
LULCC. However, the model is derived from the proportion of LULCC observed over
a given period and it does not account for temporal factors such as
macro-economic, socio-economic, etc. In this paper, we present a richer model
based on Hidden Markov Model (HMM), grounded in the common knowledge that
economic, social and LULCC processes are tightly coupled. We propose a HMM
where LULCC classes represent hidden states and temporal fac-tors represent
emissions that are conditioned on the hidden states. To our knowledge, HMM has
not been used in LULCC models in the past. We further demonstrate its
integration with other spatio-temporal models such as Logistic Regression. The
integrated model is applied on the LULCC data of Pune district in the state of
Maharashtra (India) to predict and visualize urban LULCC over the past 14
years. We observe that the HMM integrated model has improved prediction
accuracy as compared to the corresponding MC integrated model
