TY - GEN
T1 - Computational Model for Urban Growth Using Socioeconomic Latent Parameters
AU - Yadav, Piyush
AU - Ladha, Shamsuddin
AU - Deshpande, Shailesh
AU - Curry, Edward
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Land use land cover changes (LULC) are generally modeled using multi-scale spatio-temporal variables. Recently, Markov Chain (MC) has been used to model LULC changes. However, the model is derived from the proportion of LULC 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 LULC processes are tightly coupled. We propose a HMM where LULC classes represent hidden states and temporal factors represent emissions that are conditioned on the hidden states. To our knowledge, HMM has not been used in LULC 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 LULC data of Pune district in the state of Maharashtra (India) to predict and visualize urban LULC changes over the past 14 years. We observe that the HMM integrated model has improved prediction accuracy as compared to the corresponding MC integrated model.
AB - Land use land cover changes (LULC) are generally modeled using multi-scale spatio-temporal variables. Recently, Markov Chain (MC) has been used to model LULC changes. However, the model is derived from the proportion of LULC 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 LULC processes are tightly coupled. We propose a HMM where LULC classes represent hidden states and temporal factors represent emissions that are conditioned on the hidden states. To our knowledge, HMM has not been used in LULC 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 LULC data of Pune district in the state of Maharashtra (India) to predict and visualize urban LULC changes over the past 14 years. We observe that the HMM integrated model has improved prediction accuracy as compared to the corresponding MC integrated model.
KW - Hidden Markov Model
KW - Image classification
KW - Land use land cover change
KW - Logistic Regression
KW - Spatio-temporal growth factors
KW - Support Vector Machine
KW - Urban prediction model
UR - https://www.scopus.com/pages/publications/85063540504
U2 - 10.1007/978-3-030-13453-2_6
DO - 10.1007/978-3-030-13453-2_6
M3 - Conference Publication
AN - SCOPUS:85063540504
SN - 9783030134525
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 78
BT - ECML PKDD 2018 Workshops - Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Proceedings
A2 - Alzate, Carlos
A2 - Monreale, Anna
PB - Springer-Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018
Y2 - 10 September 2018 through 14 September 2018
ER -
