2-s2.0-84964038648

[vc_empty_space][vc_empty_space]

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Bandung is one of the largest growing cities in Indonesia with a high human population growth and density. The northern part which has hilly terrain with nice and beautiful scenery attracted many people to live there. This condition puts pressure to this area. Local government is very concerned with this water catchment area due to its rapid land use change. Some models have been developed to identify, monitor and predict a change on spatial pattern in the future, but none of them incorporate spatial externality and agent interaction in the built model. The aim of this paper is to explore how these two items can affect a spatial pattern of the study area. The study focuses on residential area and the simulation model is developed based on an agent based modeling. The model is used to simulate the dynamic spatial pattern. The model is cells based and it represents geographic data, i.e. land cover, terrain, location of public facilities. The cell characteristic is influenced by and influence to the neighboring cells. The hypothetical agent took a decision to seek a place to settle based on proximity of public facilities and amenities. They interact to another agent and cell under its control. Probabilistic decision-making used in the interaction process. The simulation result demonstrates that this approach can capture spatial change based on the human environment interaction.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Agent-based model,Agent-based modelling,Human population growth,Human-environment interaction,Probabilistic decision making,Public facilities,Spatial externalities,Spatial patterns[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Agent based model,Public facilities and amenities,Spatial pattern[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]