An economic agent-based model of coupled housing and land markets (CHALMS)

This paper describes a spatially disaggregated, economic agent-based model of urban land use, which is named for its innovative feature of coupled housing and land markets (CHALMS). The three types of agents—consumer, farmer and developer—all make decisions based on underlying economic principles, and heterogeneity of both individuals and the landscape is represented. CHALMS simulates the conversion of farmland to housing development over time, through the actions of the agents in the land and housing markets. Land and building structures in the housing bundle are treated explicitly, so the model can represent the effects of land and housing prices on housing density over time. We use CHALMS to simulate the dynamics of land-use changes as a representative suburban area grows. The presence of agent and landscape heterogeneity, stochastic processes, and path dependence require multiple model runs, and the expression of spatial dispersion of housing types, overall housing density, and land prices over time in terms of the most likely, or ‘average’, patterns. We find that CHALMS captures both the general tendency for diminishing population density at greater distances from the center city, and dispersed leapfrog patterns of development evident in most suburban areas of the US.     

Modeling and simulating residential mobility in a shrinking city using an agent-based approach

Shrinking cities are characterized by a huge oversupply of dwellings and resulting residential vacancies. Discussions among urban planners and policymakers in Europe have focused on the consequences of urban shrinkage following demographic transition, fertility decline and individualization. In this study, the shrinking city of Leipzig in Eastern Germany is singled out as a case basis for the study of residential mobility and land use change using agent-based modeling techniques, in which social scientists developed a concept of household types based on empirical data that form a unique base; these techniques were used to construct a data-driven, agent-based model. The spatially explicit simulation model RESMOBcity presented here ‘translates’ these empirical data via behavioral rules of households. It computes spatially explicit household patterns, housing demands and residential vacancies. Based on three scenarios, population growth, stagnation and shrinkage, we show that population might stabilize within the coming years. The number of households is expected to further increase. We demonstrate that a selective demolition of vacant housing stock can counteract the enormous oversupply of dwellings and better balance housing demand and the number of available flats. Scenario simulation shows that the model can reproduce observed patterns of population, inner-urban migration and residential vacancy in a spatially explicit manner and thus can be applied to the analysis of scenarios of demographic change in urban regions. The presented model acts as a tool supporting the testing of hypotheses in social science research and allowing the quantification of land-use scenarios in urban regions based on household choices.     

Microsimulation of Urban Development and Location Choices: Design and Implementation of UrbanSim

UrbanSim is a new urban simulation model, developed over the past several years, which is now operational in three urban areas in the United States. The model system is designed to address emerging needs to better coordinate transportation and land use planning as a result of recognition of the strong interactions between land use and transportation, increasing pressure from federal transportation and environmental legislation, and growing adoption of state growth management programs. The model system is implemented as a set of interacting model components that represent the major actors and choices in the urban system, including household moving and residential location, business choices of employment location, and developer choices of locations and types of real estate development, all subject to the influence of governmental transportation and land use policy scenarios. The model design is unusual in the degree of disaggregation of space, time, and agents, and in the adoption of a dynamic disequilibrium approach. The objective of this paper is to describe the entire system at a sufficient level of detail to convey the key specification and design choices made in implementing the system.     

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration

Urban land use change modeling can enhance our understanding of processes and patterns of urban growth that emerge from human-environment interactions. Cellular automata (CA) is a common approach for urban land use change modeling that allows for discovering and analyzing potential urban growth pathways through scenario building. Fundamental components of CA such as neighborhood configuration, transition rules, and representation of geographic entities have been examined in depth in the literature. However, trade-offs in the quantitative composition that urban gains from different non-urban land types and their dynamic feedback with the spatial configuration of urban growth are often ignored. The urban CA model proposed in this study links the quantitative composition with the spatial configuration of urban growth by incorporating a trade-off mechanism that adaptively adjusts the combined suitability of occurrence for non-urban land types based on analysis of transition intensity. Besides, a patch growing module based on seeding and scanning mechanisms is used to simulate the occurrence and spreading of spontaneous urban growth, and a time Monte Carlo (TMC) simulation method is employed to represent uncertainties in the decision-making process of urban development. Application of the model in an ecologically representative city, Ezhou, China, reveals improvement on model performance when feedback between the quantitative composition and spatial configuration of urban growth is incorporated. The averaged figure of merit and K-fuzzy indices are 0.5354 and 0.1954 with respect to cell-level agreement and pattern similarity, indicating the utility and reliability of the proposed model for the simulation of realistic urban growth.     

The Lowry model of land use simulation and its derivatives

The Lowry family of urban land use simulation models is one of the most widely used, both as an aid in scientific investigation, and as a tool for making predictions concerning growth and change in the distribution and intensity of land use activities. This paper outlines the theoretical work and modelling experience which led to the development of the Lowry family of land use simulation models and sketches the main features of its logical structure. The paper commences with a discussion of procedural relationships between modelling and planning, focusing upon difficulties which are frequently encountered when predictive models are to be designed for planning purposes. Next, the paper describes the conceptual foundations of the gravity and entropy-maximizing models of spatial interaction, followed by an explanation of how the gravity idea was applied in the Lowry model of urban land use and its successors. Finally, the last section of the paper is devoted to ITLUP, in which Lowry-type land use models embodying entropy-maximizing trip functions are linked with a transportation network simulation model.     

An empirical analysis of the influence of urban form on household travel and energy consumption

Using the 2001 National Household Travel Survey (NHTS) data, this paper empirically examines the effects of urban land use characteristics on household travel and transportation energy consumption in the Baltimore metropolitan area. The results of regression analysis show that different built environment measures lead to substantially different findings regarding the importance of urban form in influencing travel behavior. Among the built environment variables used in the analysis, accessibility provides much more explanatory power than density, design and diversity measures. Moreover, this study explores approaches to modeling the connection between urban form and household transportation energy consumption. Applying Structural Equation Models (SEMs), we found that urban form does not have a direct effect either on VMT or on vehicle energy consumption. The indirect effect, however, is significant and negative, which suggests that urban form affects household travel and energy consumption through other channels. In addition, household socio-economic characteristics, such as gender and number of vehicles, and vehicle characteristics also show significant relationships between VMT and energy consumption. This empirical effort helps us understand the major data and methodology challenges.     

Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption

Sustainability policies to mitigate transportation energy impacts on the urban environment are urgently needed. Energy prediction models provide critical information to decision-makers who develop sustainability policies to reduce energy use and emissions. We present a transportation energy model (TEM) that uses Explainable Artificial Intelligence (XAI) methods to predict household transportation energy consumption in this study. The TEM model uses data-driven approaches for household transportation energy prediction. Machine learning techniques in artificial intelligence (AI) predictive modeling have become popular due to their ability to capture nonlinear and complex relationships. On the other hand, developing comprehensive understanding the inference mechanisms in AI models and ensuring trust in their predictions is challenging. This is because AI models are mostly of high complexity and low interpretability; in other words, they are black-box models. This study presents a case study of how model transparency and explanation can be generated using the Local Interpretable Model-Agnostic Explanation (LIME) to support advanced machine learning techniques in the transportation energy field. The methodology has been implemented based on the Household Travel Survey (HTS) data, which is used to train the artificial neural network with a relatively high degree of accuracy. The importance and effect (local explanation) of HTS inputs (such as household travel, demographics, and neighborhood data) on transportation energy consumption for specific traffic analysis zones (TAZs) are analyzed. The results are valuable to promote intelligent and user-friendly transportation energy planning models in urban regions across the world.     

ACCESS: An agent-based model to explore job accessibility inequalities

This paper presents ACCESS, an agent-based model for exploring job accessibility inequalities among different social groups. ACCESS allows for investigation on the impact of public transport and land use policies on the residential location of the working population and their accessibility to job opportunities. ACCESS can be adapted to different realities, allowing to represent societies with diverse socioeconomic disparities. A utility function composed of job accessibility and neighborhood status is maximized by agents during the residential location choice process. The model outputs include Lorenz curves considering the accessibility dimension, as well as Gini metrics to support the analysis of interventions impacts on accessibility inequalities. An empirical case study is performed on the municipality of Sao Paulo, which is characterized by high levels of inequality. Five experiments were simulated considering three different socio-occupational groups. The first experiment includes (i) new public mass rail transport lines, and the other four experiments consider the new transport infrastructure from the first experiment and add (ii) new social housing location strategies; (iii) new job locations; (iv) new jobs and different social housing supplies and location strategies; and (v) provision of social housing based on a government housing program. The results show that ACCESS allowed the residential location of different social status groups to be depicted with a high correlation to the observed situation. Regarding the case study, the results indicate that only having interventions on transport system is insufficient to provide a significant change in terms of inequality. Better results that impact inequality are reached with public mass rail transport interventions associated with land use policies with different social housing and job location programs.     

Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata,India

Urban growth pattern has a direct influence on urban development processes both at the city and neighbourhood levels, but on the other hand growth pattern is also affected by land and housing policy, current built-up area, available developable area, change in household count, population, economy, and others. Remote sensing and GIS techniques can be used to analyse and model the urban growth pattern. This study was carried out to identify and analyse the urban growth pattern of Kolkata, India. Several statistical methods were applied to recognize and analyse this pattern, which shows increasing dispersed development of the city with the declining population growth rate. Apart from the derived results, this study also shows the potentiality of remote sensing and GIS techniques for the analysis of urban growth patterns.     

Development of SLEUTH-Density for the simulation of built-up land density

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation of built-up land density may help planning and development authorities in making better land developmental decisions, making appropriate provisions for services, long term land use planning, and allocation of natural resources. In the recent past, various efforts have been made to assess built-up density in terms of the densification of built-up activities at the city scale in terms of floor space indices, building density, residential density (number of housing units per hectare) and urban volume. However, only limited research was reported on the simulation and prediction of spatially distributed built-up land density. In the present study, an algorithm is developed to simulate the spatial distribution of built-up land density as a function of a set of selected urbanization explanatory variables. Its application has been demonstrated using a newly developed version of the SLEUTH model (SLEUTH-Density) to simulating the built-up land density for Ajmer city in India. Development of SLEUTH-Density included designing a density algorithm, writing the programming code, integrating the code with the existing SLEUTH model, and testing the algorithm. The model results were validated indirectly using few built-up land density indices and directly through field verification, which were found to be in good agreement with the simulated built-up land density from SLEUTH-Density     

Transportation and Regional Economic Development: Analysis of Spatial Spillovers in China Provincial Regions

The role of spillover effect of transportation endowment on regional economic development is analyzed in this paper. We adopt the reduced form from the Solow growth model to estimate spillover effect from transportation. The panel database in use incorporates provincial gross regional product (GRP), labor and capital supply, and transportation investment information from 1985 to 2012 in China. The results confirm positive and significant spillover effect in Chinese provinces. In this paper, non-homogeneous spillover effects are captured in the empirical regression by the use of spatial weighing methods based on provincial economics and similarity as well as geographic connection. Highly positive spillovers are observed between economically similar provinces. However, for those under-developed provinces, high network connectivity often results in low or negative spillovers. The mobility and migration of production factors are believed to be the sources of the negative spillovers, while the industrial reallocation and market expansion contribute to the positive spillovers.     

Forecasting the pattern of urban growth with PUP: a web-based model interfaced with GIS and 3D animation

This paper describes a model (PUP — Projections for Urban Planning) implemented to forecast the location of housing development and population growth on the fringes of large cities. The model couples the land use and housing unit methods of population forecasting in a GIS framework that delivers a seamless interface between data assembly, modelling, visualisation and analysis. Work has focussed on incorporating spatial relationships, calibration, and visualisation. Three factors are accounted for: land availability; accessibility to facilities and other stimuli; and adjacency to existing development. These are weighted and combined to determine the probable location of future development. Forecasts are available as: GIS compatible files; tables; or 3D animations with interactive querying. The model has been implemented in Adelaide but its generic structure yields a flexible, interactive forecasting system that can be adapted to provide decision support for urban planning in a range of urban settings.     

Dynamic agent based simulation of welfare effects of urban disasters

An agent based model for assessing the welfare impacts of urban disasters is presented. This couples a population allocation algorithm with a simulation platform. The fully articulated model fuses both bottom-up (locational choice for workplace, residence and daily activities) and top-down (land use and housing price) protocols. This study moves beyond current research by addressing economic welfare consequences of urban disasters. The resilience capabilities of different income groups are identified. This is illustrated for the Jerusalem central business district. Empirical results at the micro-scale suggest that physical destruction leads to a zero-sum game within the housing market in which wealthier residents hold an advantage over the poor. This results in the transformation of neighborhoods and displacement of poor and vulnerable populations. Low income groups lose both physical ground and the social support systems that go with location. Policy implications of these findings are discussed.     

智能家居控制系统的设计及实现

随着计算机技术、网络和信息技术的不断发展,以及人们对生活要求的不断提高,享受智能化的家居生活已经成为可能,并且成为必然的趋势。本文主要介绍一种基于32位嵌入式处理器ARM9平台和GPRS无线网络通信技术的智能家居控制系统,该系统主要针对普通住宅小区家庭用户而设计,它可以对用户住宅内的安全状况进行集中监控和管理,并实现对家用电器设备的远程控制。     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Spatial Autocorrelation Panel Regression: Agricultural Production and Transport Connectivity

Transport infrastructure is an important determinant of agricultural productivity. Using various new spatial data, the paper measures different types of transport accessibility and estimates their impacts in Ethiopia. The paper takes advantage of a historical event that Ethiopia, a landlocked country, ceased freight rail operations connecting its capital and the main seaport in the late 2000s. Using the substantial changes in transport accessibility, the spatial autocorrelation panel regression is applied to show that the proximity to close markets and the access to the port are of particular importance for agricultural production. The elasticity is estimated at about ?0.05 to ?0.13, depending on type of accessibility. It is also found that there are considerable spillover effects that come from the spatial autocorrelation errors, meaning that crop production at one place is affected by its neighborhood environment, possibly including land fertility and weather conditions.

     

Measuring Urban Performance

Abstract

The concept of urban performance is introduced and discussed and specific quantitative performance measures are formulated which reflect the availability of certain desired urban attributes to different groups or residents. The measures are sensitive to the geographical distribution of types of land use activity, to the nature of the transportation system, to the behavioral characteristics of different resident groups, and to social and economic policies. Hence the measures can be influenced by physical and social programs along with endogenous urban development. Urban data items are suggested for the calculation of the urban performance measures, and the display and use of the measures as an aid to urban planning is discussed.     

Research and application of 3D visualization and Internet of Things technology in urban land use efficiency management

Land is the basis for human beings to engage in economic and social activities, but the continuous acceleration of the process of economic development and urban expansion has brought about a series of ecological and urban problems. Therefore, the simulation of urban spatial expansion is very important for the rational planning of urban land and the promotion of sustainable urban development. The three-dimensional visualization of urban land allows users to have a perceptual understanding of urban construction, and enables decision makers, designers and users to have a more vivid and rational understanding of the current state of urban construction and planning and design blueprints. Based on this, this article combines three-dimensional imaging and Internet of Things technology to carry out the research on urban land utilization. First of all, this article discusses image modeling techniques such as texture mapping and panoramas, and applies these techniques to the model reconstruction of complex urban land. The model effectively combines graphics-based and image-based model reconstruction technology to construct a highly realistic three-dimensional urban landscape. Secondly, this article combines the Internet of Things and wavelet analysis to design an Internet of Things architecture for urban land use efficiency management. Finally, on the premise of taking into account the important results and the status quo of urban development, the countermeasures and suggestions for increasing the urban land use rate are put forward.     

Entangled footprints: Understanding urban neighbourhoods by measuring distance,diversity, and direction of flows in Singapore

Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective.We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood's position within the transportation network in future work.