论文标题
使用社会经济潜在参数用于城市增长的计算模型
Computational Model for Urban Growth Using Socioeconomic Latent Parameters
论文作者
论文摘要
土地利用土地覆盖变化（LULCC）通常是使用多尺度时空变量建模的。最近，马尔可夫链（MC）已用于建模lulcc。但是，该模型源自在给定时期内观察到的LULCC的比例，并且没有说明诸如宏观经济，社会经济等宏观经济，社会经济等的时间因素。我们在本文中提出了一个基于隐藏的马尔可夫模型（HMM）的更丰富模型，基于共同的经济，社交和LULCC流程的共同知识是紧密耦合的。我们提出了一个hmm，其中lulcc类代表隐藏状态，而暂时的fac-tors表示在隐藏状态下的排放。据我们所知，过去曾在LULCC模型中使用过HMM。我们进一步证明了它与其他时空模型（例如逻辑回归）的集成。综合模型应用于马哈拉施特拉邦（印度）浦那地区的LULCC数据，以预测和可视化过去14年中的Urban Lulcc。我们观察到与相应的MC集成模型相比，HMM集成模型的预测准确性提高了
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
