agent based model

According to the theory of natural movement, street networks interact with pedestrian movement and walkability. In addition to permeability criteria, visual perception has an impact on pedestrian preferences and behaviors by providing a capacity for understanding and wayfinding. This research aims to investigate the correlation between the visual and spatial characteristics of the environment in the Jolfa neighborhood of Isfahan city. The set of space syntax tools, including Axial, Isovist, and Visual Graph Analysis, were used to quantify and interpret the configurational and visual criteria in the historical area of Jolfa. The Spearman correlation coefficient was used to calculate the correlation between both integration and connectivity with isovist area, isovist perimeter, compactness, occlusivity, through-vision, and visual clustering. The results show that, in addition to integration, connectivity, and intelligibility, visual properties (i.e., isovist area, isovist perimeter, and occlusivity) had a significant impact on movement patterns in this area. To Validate the results, the agent-based model was used to compare the space syntax simulation of the pedestrian movement with the actual environment. It was shown that visual features derived from space syntax can predict pedestrian movement in the real world. Visual characteristics are complementary to spatial configuration and can help with spatial perception, wayfinding, and pedestrian movement.

In recent decades due to the rapid and unplanned growth of cities, urban management has been faced with several issues, including models and patterns of urban development and the selection of optimal options for location. Neyshabour is known as the second most populous city in Khorasan Razavi province. Due to reduced rainfall and drought, agriculture has lost its prosperity and most of the rural population has migrated to the city. Urban development research has been done by the ABM model using the factors affecting Neyshabour urban development. These factors include: the dominant position of desirable socio-Access-quality road access to green space and privacy watercourse Services and usage is taken into account. The ABM model is a set of independent factors with goals and capabilities that interact with each other. To validate the model, the Markov-cellular method based on Landsat 7 and 8 satellite images has been used for 17 years. Based on the results of urban development simulation using the ABM, the area of the urban area has increased from 2018 to 2035 with a growth rate of 19.8%, and in the Markov-cellular model, the area of the urban area from 2018 to 2035 with a growth rate of / Will increase by 23%. Accordingly, these results do not differ much from each other. The results show that in the implementation of these two models, The ABM model suggests that social influence is dominant class and carefully detailed model compared with Markov-cellular automation is acceptable

Complex interactions between human decision-makers and their biophysical environment can be observed in land-use systems. These complexities are due to the differences between biophysical and socio-economic variables. For Modeling of human decision-making, we need to know interactions between landscape, community, or ecosystems. In reality, humans make decisions using variety of strategies. We need to simplify the complex interaction between all individual agents and their environment by formulating an agent typology. In this paper, given the complexity of the decision-making in agent based models, the agricultural land use changes are simulated by the multinomial logistic regression model to determine the socio-economic and environmental factors. Also the proportional random rules use to implement the bounded rationality law for unique individual decision making. Considering the particular environmental conditions of the region and the severity of the risk of desertification, the economic, social, physical factors and the chemical parameters of soil along with other environmental factors in the decision-making processes were investigated.
Diverse data (including GIS and household data) were used for initializing the coupled human–landscape system and farmer household decision making simulations. GIS data consist of landscape agents (grid cell or patch): Land use/cover (based on Landsat 8), soil Physico-chemical properties (EC, SAR, PH (pH), texture, moisture), institutional variables (i.e. ownership, village territory), and topography. Household data consist of socioeconomic attributes: labour force, educational status, income structure, and land properties. They were derived from an intensive household survey conducted in Segzi plain in Isfahan province (central Iran) during the spring 2013. The agent-based decision-making method has been presented by Le (2005). For determining decision-making approach, a mechanism of livelihood groups dynamics were considered as follows. At first, principal component analysis (PCA) was used to identify key factors differentiating household characteristics. These factors were then employed to classify the population into certain household groups using K-Means Cluster Analyses. The identified agent groups were interpreted and their types specified. Regression logistic multinomial model (M-logit) was employed for land-use choices modeling in each typological household agent group. The dependent variable of the model is land-use choice by farming households (Puse). The independent variables of the M-logit model include two groups of spatial variable and socioeconomic characteristics of farming households. Environmental features of lands were defined including Pwet ( soil moisture), Pslope (derived from Digital Elevation Model), Pelev (elevation), PEC (Electrical conductivity as salt factor), Pgroundwater (measuring the reduction of ground water), PPH (PH), PSAR (Sodium Absorption Ratio). Socioeconomic characteristics of farming households that influence farmer's decisions, include Hage (the age of the household head), Hedu (educated farmers), Hincome/pers (annual gross income per capita), Hholding/pers (land holding per capita), Hcultivation/pers (land cultivating per capita), Hlabor (number of workers of the household), and Hdepend(family members of the workers).
We reduced the dimensionality of 14 potential criteria by using PCA. The six (6) principle components were extracted with total Eigenvalues greater than 1.0, explaining 77.4 % of the total variance of original independent variables. The PC1 was strongly correlated to land variables: Hholding=0. 911, Hcultivation=0. 925. The principle components 2 (PC2), 3 (PC3), 4 (PC4), 5(PC5) were most weighted by percentage income from other off-farm activity factors (Hinother=0.843), household size (Hsize=0.833), percentage income from grain (HinGrain=0.773), percentage income from wheat (HinWheat=0.898), respectively.
The K-means run extracted three groups. The group I consists of households which are rich regarding both land resources and income. The group II includes households with average livelihood standard and the group III comprises the poorest households having the lowest amount of land and income. After determining the typological livelihood group, the variables affecting the decision-making was identified using the M-logit model. The effect coefficients were estimated with respect to the fallow land, i.e., the base case. The chi-square test shows that the empirical M-logit model is highly significant (p

In this research, a model for simulating of residential segregation pattern is presented via integration of GIS with agent-based models, and is implemented on real data of an area in north-west of Tehran. For this purpose, at first effective parameters regarding segregation in Tehran that is a mixture of social-economical factors and environment was determined and amount of their effects with AHP method was set. Considering the raster-based approach in modeling, maps related to these parameters was produced in ArcGIS medium. By programming in Netlogo software the mentioned model was developed .In this model, with a primary assumption, people of society were classified in 4 socio-economic groups with specific attributes and behaviors which by their own decisions- micro level actions- affect the segregation pattern- effects in macro level-. The pattern of urban segregation of area was simulated for a period of 20 years between 1986 and 2006. The model was calibrated by changing the important parameters of Schelling model, neighborhood radius and frequency of the same type neighborhoods, and some other parameters as size of pixels, price of parcels, rate of population increase etc. Validation of the proposed model has been done by counting the correct estimated pixels of total pixels. Respectively 62.5 % verifies the accuracy of the models.