Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal

The SLEUTH model (slope, landuse, exclusion, urban extent, transportation and hillshade), formerly called the Clarke Cellular Automaton Urban Growth Model, was developed for and tested on various cities in North America, including Washington, DC, and San Francisco. In contrast, this research calibrated the SLEUTH model for two European cities, the Portuguese metropolitan areas of Lisbon and Porto. The SLEUTH model is a cellular automaton model, developed with predefined growth rules applied spatially to gridded maps of the cities in a set of nested loops, and was designed to be both scaleable and universally applicable. Urban expansion is modeled in a modified two-dimensional regular grid. Maps of topographic slope, land use, exclusions, urban extents, road transportation, and a graphic hillshade layer form the model input. This paper examines differences in the model's behavior when the obviously different environment of a European city is captured in the data and modeled. Calibration results are included and interpreted in the context of the two cities, and an evaluation of the model's portability and universality of application is made. Questions such as scalability, sequential multistage optimization by automated exploration of model parameter space, the problem of equifinality, and parameter sensitivity to local conditions are explored. The metropolitan areas present very different spatial and developmental characteristics. The Lisbon Metropolitan Area (the capital of Portugal) has a mix of north Atlantic and south Mediterranean influences. Property is organized in large patches of extensive farmland comprised of olive and cork orchards. The urban pattern of Lisbon and its environs is characterized by rapid urban sprawl, focused in the urban centers of Lisbon, Oeiras, Cascais Setúbal, and Almada, and by intense urbanization along the main road and train lines radiating from the major urban centers. The Porto Metropolitan Area is characterized by a coastal Atlantic landscape. The urban pattern is concentrated among the main nuclei (Porto and Vila Nova de Gaia) and scattered among many small rural towns and villages. There are very small isolated patches of intensive agriculture and pine forests in a topography of steep slopes. These endogenous territorial characteristics go back in time to the formation of Portugal — with a “Roman-Visigod North” and an “Arabic South” [Firmino, 1999 (Firmino, A., 1999. Agriculture and landscape in portugal. Landscape and Urban planning, 46, 83–91); Ribeiro, Lautensach, & Daveau, 1991 (Ribeiro, O., Lautensach, H., & Daveau, S., 1991. Geografia de portugal (4 Vols., published between 1986 and 1991). Lisbon, Portugal: João Sá de Costa)]. The SLEUTH model calibration captured these city characteristics, and using the standard documented calibration procedures, seems to have adapted itself well to the European context. Useful predictions of growth to 2025, and investigation of the impact of planning and transportation construction can be investigated as a consequence of the successful calibration. Further application and testing of the SLEUTH model in non-Western environments may prove it to be the elusive universal model of urban growth, the antithesis of the special case urban models of the 1960s and 1970s.     

Simulating the urban spatial structure with spatial interaction: A case study of urban polycentricity under different scenarios

Polycentric urban development is gaining momentum in both scholarly research and real-life practice. This brings new demand for planning support systems to simulate and analyse the urban spatial structure in terms of polycentricity under various urban policy scenarios. With the help of emerging urban data, urban simulation techniques, and network science, this study proposes a workflow to simulate the urban spatial structure with spatial interaction as a part of the planning support system. Using Singapore as a case study, this study has explored the resulting urban spatial structure with four employment distribution strategies. The results suggest that planning practices impact urban spatial structure and its spatial interaction by redistributing urban morphological elements, such as employment in this study. Also, our results show that the physical urban spatial structure and spatial interaction are closely related. These results reinforce the role of urban planning practice to achieve a more sustainable and coherent urban built environment. Through this empirical evidence, our workflow exemplifies the potential of the planning support system to help urban planners and governments understand their urban policy regarding urban polycentricity.     

Energy load superposition and spatial optimization in urban design: A case study

Building energy consumption accounts for a large portion of total energy-use in a city or a regional district. However, energy load spatial distribution has seldom been considered during urban design phase. And energy conservation and energy efficiency measures pay more attention to individual building than buildings in a district or regional space as a whole. If buildings with different functions are mixed together and share same energy system, the savings on system capacity and peak electricity load can be significant. In this paper, a load superposition concept is proposed. The term ‘superposition’ refers to overlapping of energy demand load curves from different buildings and so that the total peak is smaller than the sum of individual peaks. Three spatial optimization methods of demand side load management and three different schemes of energy systems are proposed in this paper. And economic analysis is recommended to evaluate the different energy systems. The applicability of different approaches and the significance of load superposition was analyzed and elaborated through a case study to offer planners a feasible way for evaluating the potential of load spatial optimization.     

Integrating supercomputing clusters into education: a case study in biotechnology

The Journal of Supercomputing - The integration of a Supercomputer in the educational process improves student’s technological skills. The aim of the paper is to study the interaction between...     

Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: A case study in Kampala,Uganda

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.     

Integrating value modeling and legal risk management: an IT case study

Information Systems and e-Business Management - Companies need to be able to demonstrate compliance with rules and regulations, especially start-ups who typically do not have the legal expertise to...     

Analysis of traffic hazard intensity: A spatial epidemiology case study of urban pedestrians

Traffic safety studies have underscored the hazardous conditions of pedestrians in the United States. This situation calls for increased public awareness of the pedestrian safety issue and better knowledge of the main factors contributing to traffic hazard for urban pedestrians. The purpose of this spatial epidemiology research is to gain greater insights into the geographic dimension exhibited by the intensity of traffic collisions involving urban pedestrians. Pedestrian crashes are studied in Buffalo, NY for years 2003 and 2004. Factors of hazard intensity are determined and compared for three age cohorts as well as for collisions occurring at intersections versus mid-block locations. Physical road characteristics and density of development, as well as socio-economic and demographic variables and potential trip attractors are examined. Spatial regression models are used to account for spatial dependencies. Econometric analysis underscores that all classes of environmental factors tested are significant drivers of pedestrian traffic hazard intensity. Results of the geographic analysis indicate that young and adult pedestrian traffic hazard intensities follow rather distinct logics. In addition, intersection and mid-block crashes differ by their socio-economic correlates, as well as their spatial distribution in the urban fabric.     

Integrated modelling of urban spatial development under uncertain climate futures: A case study in Hungary

To provide fundamental decision support information for climate risk assessment in Hungary, an urban spatial development model of land cover change and population age structure dynamics was developed and applied to local integrated scenarios of climate change and stakeholder-derived socio-economic change. The four integrated scenarios for Hungary produced contrasting projections for urban patterns to 2100, but peri-urbanisation around Budapest was estimated to occur under all scenarios, together with a decline in working age population in the centres of the capital and major towns. This suggests that future urban planning needs to take into consideration the potential for underutilised urban infrastructure in the centre of the capital and pressures for social service provisioning in its outskirt. The integrated scenarios and model developed can be used in future studies to test the effectiveness of inter-sectoral policy responses in adapting urban planning to multiple climate and socio-economic challenges.     

Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations

Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of ～22km² per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.     

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.     

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid‐scale level (5–25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977–2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.     

Business-oriented service modeling: A case study

Based on our hand-on experiences in developing a service-based problem-solving environment for bioinformatics research, we present in this paper a particular approach called VINCA4Science to business-oriented modeling of service functionalities, to Web services virtualization and to user-centric utilization of modeled artifacts. Some practical effects regarding the improvement of domain stability, efficiency and scalability are illustrated.     

Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore

Natural language search engines should be developed to provide a friendly environment for business-to-consumer e-commerce that reduce the fatigue customers experience and help them decide what to buy. To support product information retrieval and reuse, this paper presents a novel framework for a case-based reasoning system that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. Furthermore, the case retrieval agent integrates short-text semantic similarity (STSS) and recognizing textual entailment (RTE). The proposed approach was evaluated using competitive methods in the performance of STSS and RTE, and according to the results, the proposed approach outperforms most previously described approaches. Finally, the effectiveness of the proposed approach was investigated using a case study of an online bookstore, and according to the results of case study, the proposed approach outperforms a compared system using string similarity and an existing e-commerce system, Amazon.     

CFD assisted modeling for control system design: A case study

This paper presents a novel modeling approach of coupling transient computational fluid dynamics (CFD) simulation with system identification for control system involving fluid flow and heat transfer. In order to illuminate the feasibility of this method, a fluid flow and heat transfer related process, i.e. a three dimension (3-D) spatio-temporal air temperature distribution and input (inlet air temperature) dependent process in the desert climate chamber, is considered. The distributed parameter models of the chamber temperature are identified using transient CFD simulation results and are then validated against the results obtained from the CFD simulations with high RT² (more than 0.97) and negative Young’s information criterion (YIC, less than ?11.8). The PI controllers embedded in CFD simulation are then developed based on the models. The performance of the closed-loop systems is also evaluated within the full-scale CFD model. The results show that CFD-based system identification is feasible to model fluid flow and heat transfer related processes.     

Collaborative optimization of rural residential land consolidation and urban construction land expansion: A case study of Huangpi in Wuhan,China

The synergetic development of urban and rural construction land is always an important issue. We propose a collaborative optimization model (COMRU) of rural residential land consolidation and urban construction land expansion, which is a coupling model of cellular automata (CA), genetic algorithms (GA), and the Lewis turning point theory. This model regards the rural population transfer as a scenario and generates a new quantity and space allocation system for the population and the land-use types in the study area. The optimized result will balance the development of urban and rural construction lands to ultimately reduce the income gap between urban and rural areas and promote the rationality of the spatial distribution of urban and rural construction lands. We applied COMRU to Huangpi District in the city of Wuhan, the capital of Hubei Province, People's Republic of China and obtained three important results: (1) After optimization, the scattered rural settlements were effectively consolidated and large amounts of land resources were released, thereby supplementing cultivated and urban construction lands; (2) The urban–rural income ratio decreased significantly, indicating a considerable reduction in the income gap between the urban and rural areas; (3) The structure and function of the construction lands were improved, leading to the improved equity of urban and rural public services. The final space optimization allocation program generated by COMRU provides a reference for the sequence of rural settlement consolidation and urban spatial planning.     

Playful public participation in urban planning: A case study for online serious games

The aim of this paper is to study the implementation of online games to encourage public participation in urban planning. Its theoretical foundations are based on previous work in public participatory geographical information systems (PP GISs), play and games, with a special focus on serious games. Serious games aim to support learning processes in a new, more playful way. We developed the concept of playful public participation in urban planning, including playful elements such as storytelling, walking and moving, sketching, drawing, and games. A group of students designed an online serious public participatory game entitled NextCampus. The case study used in NextCampus was taken from the real-world question of a possible move of a university campus to a new location in the city of Hamburg, Germany. The development of the serious public participatory game NextCampus resulted in a physical prototype, user interface design, and a computational model of the game. The NextCampus game was tested with the help of two groups of urban planning students and presented to three external experts who provided valuable recommendations for further development. The critical comments questioned the level of complexity involved in such games. The positive comments included recognition of the potential for joy and the playfulness a game like NextCampus could evoke.     

A spatial production analysis of Chinese regional banks: case of urban commercial banks

Urban commercial banks are regional banks which have gained tremendous importance in the last two decades in China. There is, however, a lack of research on regional banking, especially on the Chinese regional banking industry. Therefore, using an innovative spatial approach, this paper investigates the efficiency of 65 Chinese urban commercial banks across 26 regions during 2013–2017. A key finding for our sample is the significant spatial dependence of loans of Chinese urban commercial banks with their neighbouring regions’ banks. Short‐run efficiency is increasing during the research period. For regions with less than three urban commercial banks, the average efficiencies are stable and relatively high. However, regions with more banks have both the highest and lowest efficient banks at the same time. These interesting results fit with the development process of Chinese urban commercial banking, in which the market restructuring has contributed to banks’ efficiency.     

A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules

Cellular automata (CA) models are extensively applied in urban growth modeling in different forms (i.e., pixel or patch). Studies have reported that the patch-based approach can achieve a more realistic urban landscape. However, they are subjected to uncertainties due to a variety of stochastic processes involved, which weakens their effectiveness on urban planning or decision making. Here, we propose a new patch-based urban growth model with heuristic rules that employed logistic CA model with a watershed segmentation algorithm (Segmentation-Patch-CA). The segment objects derived from features of urban CA model were regarded as potential patches for conversion, through defining a utility function that considered both the suitability and heterogeneity of pixels within the patch. Thereafter, two different urban growth types, i.e., organic growth and spontaneous growth, were identified and simulated separately by introducing a landscape expansion index (LEI) that built on neighborhood density analysis. The proposed Segmentation-Patch-CA was applied to Guangzhou City, China. Our results revealed that the proposed model produced a more realistic urban landscape (96.00% and 97.38%) than pixel-based (45.14% and 74.82%) for two modeling periods 2003–2008 and 2008–2012, respectively, when referring to an assembled indicator that closely related to urban patterns (e.g., shape, size, or distribution). Meanwhile, it also achieved a good performance when comparing to other patch-based urban CA models but with less uncertainty. Our model provided a very flexible framework to incorporate patches using segments or self-growth based on pixels, which is very helpful to future urban planning practices.     

Optimization model for urban air quality policy design: A case study in Latin America

Air pollution is a major problem caused by urban growth and poses a high risk to human health. To mitigate the negative impact of pollution, some cities have implemented air quality strategies that regulate emissions from multiple sources. However, when such strategies are applied in isolation they may fail to comply with stringent environmental goals. To support the development of comprehensive urban air quality policy, we propose an optimization model that suggests a portfolio of mitigation actions that captures realistic aspects of air quality policy-making such as implementation cost, pollution goals, interdependencies between alternatives, and managerial and regulatory constraints. We illustrate the advantages of our model with a real case study in Bogotá, Colombia's capital and one of the largest cities of Latin America, where new legislation established a PM₁₀ air quality standard. To achieve the environmental goal, our model suggests an optimal portfolio consisting of six mitigation actions targeting mobile and stationary sources. Interventions in the transport sector alone generate 70% of the emissions reduction. The PM₁₀ goal is achieved with a net incremental cost of US$1 billion with respect to the no-intervention scenario. The optimization model and results presented in this article were formally adopted as the official air quality policy in the city.     

Wireless monitoring and tracking system for vehicles: A study case in an urban scenario

This paper describes the application of a Wireless Traffic Monitoring and Tracking system in the Spanish city of Granada, as an approach for addressing important tasks in the field of Smart Traffic. To this end, several nodes of the so-called MOBYWIT system have been deployed at important urban points. They collect real-time vehicles’ movement information based on Bluetooth signals detection. The gathered data have been processed in several ways, showing some of the applications that the system has, such as the composition of Origin/Destination matrices, the computation of accurate displacement times, or the estimation of real traffic in short terms by means of Time Series Forecast. The obtained results validate the system and proves its value as a tool for the urban traffic flow monitoring, analysis and prediction, which could be used as a part of an intelligent transportation system.