4.2.2 Cellular automata (CA)
CA is a dynamical discrete system in space and time that operates on a uniform grid based space (Batty 1976; Alkheder and Shan 2005; Hand 2005). It was first introduced in the 1940’s by John Von Neumann, the founder of the game theory 11 , and Stanislaw Ulam, who worked in the Manhattan project 12 and made intensive research in the field of Monte Carlo Simulation (Hand 2005; Pinto and Antunes 2007). Since its advent, it has been used to model a wide range of phenomena due to its ability to represent spatial process ranging from forest fires to epidemics, from traffic simulation to regional-scale urbanization (Pinto and Antunes 2007). It has also been implemented in various landuse models to simulate multiple landuse types and provides a powerful tool for modelling the dynamic nature of the landusechange. There are, however, profound challenges to CA-based urban simulation: complex urban system and cells are usual defined by a binary state (Xie 2003). The term CA itself is evolved from the fact that it consists of cells and transition rules that are applied to determine the state of a particular cell. Each cell depends on its state in the previous period and the state of its immediate neighbor according to the rules applied. This rule is applied to all cells on the grid. The neighboring cells are often defined as the entire set of eight adjacent cells (Moore neighborhood) or as a set of four adjacent cells usually located in the main cardinal points (von Neumann neighborhood) (Hand 2005). The von Neumann and Moore neighborhoods relationships are shown in Figure 4.1.
It is well known that optimal road pricing may yield a social and environmental surplus . Furthermore, in some cases, the added value of the measure to the economy and efficiency has been demonstrated through gains in the value of time and reduced congestion . In this case, for the design of the cordon toll, we decided to follow a similar scheme of the cordon toll implemented in Stockholm, which is usually considered as a successful casestudy  (Table 7). The MARS model simulates the cordon toll policies by increasing the cost of making certain trips, in this case, the area proposed for the cordon toll simulation includes zones 1 to 7 (Figures 2 and 6), and all trips entering in these zones are affected. In addition, and following the Stockholm cordon toll scheme, we implemented improvements in PT service frequencies, which were increased by seven per cent. On the other hand, the acceptability of pricing policies potentially increases along the time, and this can change the effectiveness of the policies . To avoid this effect, it is sometimes recommended to increase the price over time. In the case of Stockholm, the initial price in 2006 was 20 SEK at peak hours, and the price increased to 35 SEK in 2016 . We followed a similar approach for a 15-year simulation, from 2016, which is the initial year of the policy implementation, to 2031, which is the end year of the simulation process. Other authors have also carried out analysis of cordon toll policies using the MARS model before [12,13], where more details about the implementation process can be consulted. Some features about the cordon toll implementation are presented in Table 7.
Urban growth, particularly the movement of residential and commercial landuse to rural areas at the periphery of metropolitan areas, has long been considered as a sign of regional economic vitality ( Yuan et al, 2005 ). However, its importance become unbalanced with impacts on ecosystem, greater economic differences and social fragmentation. It can be defined as the rate of increase in urban population. Dynamic processes due to urbanchange, especially the tremendous worldwide expansion of urban population and urbanized area, affect both human and natural systems at all geographic scales (Brockerhoff, 2000). Araya and Cabral (2010) have shown that urban growth in Setúbal and Sesimbra, Portugal has been increased significantly between 1990 and 2000 by 91.11%. Much of this growth, however, was towards the periphery of urban areas due to the coalescence of a number of smaller settlements as well as through the consumption of agricultural land. The growth was predicted to continue in the future and would have wide range of consequences on natural resources. Their study also noted that population growth was one of the major driving factor for such rapid growth in the study area.
Satellite imagery has been well utilized in the natural science communities for measuring qualitative and quantitative terrestrial land-cover changes. Landsat data are most widely used for studying the Landuse and Land cover changes. K. C. Seto, C. E. Woodcock, C. Song, X. Huang, J. Lu And R. K. Kaufmann, have monitored the land-usechange in the Pearl River Delta using Landsat TM. J. Li and H.M. Zhao have studied the UrbanLandUse and Land Cover Changes in Mississauga using Landsat TM images. Tamilenthi1, J. Punithavathi1, R. Baskaran1 and K. Chandra Mohan have studied the dynamics of urban sprawl, changing direction and mapping using a casestudy of Salem city, Tamilnadu, India. H.S. Sudhira, T.V. Ramachandra and K.S. Jagadish , studied about Urban sprawl metrics, Land cover dynamics and modelling using GIS for Udupi Mangalore. M. Turker and O.Asik studied LandUseChange Detection At The Rural- Urban Fringe Using Multi-Sensor Data In Ankara, Turkey. Bassam Saleh and Samih Al Rawashdeh studied about Study of urban expansion in Jordanian cities using GIS and RS. All the researchers identified that urban environments are most dynamic in nature. Information on urban growth, landuse and land cover changestudy is very useful to local government and urban planners for the betterment of future plans of sustainable development of any area.
Abstract. Seismic risk mitigation comprises of land-use planning policies that enable risk reduction in areas exposed to earthquakes. Thus, the assessment of land-use plans re- garding urban growth in seismic prone areas is very impor- tant. This article analyses the urban expansion of Vila Franca do Campo (island of S. Miguel, Azores, Portugal) from 1994 to 2005 based on ortophotomaps interpretations and simu- lates a scenario of urban growth for the year 2016 with a Land-use and Cover-Change (LUCC) model (Geomod). The goal is to evaluate the potential impact of land-use plans in managing urban growth and promoting seismic risk mitiga- tion. Results indicate that the urban expansion, between 1994 and 2005, was done according to the Municipal Master Plan (MMP) restrictions. The scenario modelled for the year 2016 is potentially stricter for urban growth because it adds to the previous plan the constraints defined by the South Coast Management Plan (SCMP) that entered into force in 2007. In both time periods, a continuing urban growth towards seis- mic areas was identified. The absence of seismic risk mitiga- tion policies and measures on both plans may contribute to increase the seismic hazard exposure and vulnerability. The results of this study strongly suggest the reformulation of fu- ture land-use plans to include seismic risk mitigation goals and policies.
Recent improvements in satellite image quality and availability have made it possible to perform image analysis at much larger scale than in the past. Satellite imagery has been well utilized in the natural science communities for measuring qualitative and quantitative terrestrial land-cover changes. Landsat data are most widely used for studying the Landuse and Land cover changes. K. C. Seto, C. E. Woodcock, C. Song, X. Huang, J. Lu And R. K. Kaufmann, have monitored the land- usechange in the Pearl River Delta using Landsat TM. J. Li and H.M. Zhao have studied the UrbanLandUse and Land Cover Changes in Mississauga using Landsat TM images. Tamilenthi1, J. Punithavathi1, R. Baskaran1 and K. Chandra Mohan have studied the dynamics of urban sprawl, changing direction and mapping using a casestudy of Salem city, Tamilnadu, India. H.S. Sudhira, T.V. Ramachandra and K.S. Jagadish , studied about Urban sprawl metrics, Land cover dynamics and modelling using GIS for Udupi Mangalore. M. Turker and O.Asik studied LandUseChange Detection At The Rural- Urban Fringe Using Multi-Sensor Data In Ankara, Turkey. All the researchers identified that urban environments are most dynamic in nature. Information on urban growth, landuse and land cover changestudy is very useful to local government and urban planners for the betterment of future plans of sustainable development of any area.
2.3 DESCRIPTION OF ZOODRM
The grid resolution is 2 km by 2 km and Figure 7 shows the model grid for the whole of England and Wales. Due to the resolution of the figure, the details of the grid can’t be seen over England and Wales so details are provide for a northern catchment the Tees and a catchment in the south of England, the River Thames. A soil water balance is calculated at nodes which are located where the grid lines cross. Landuse mapping (Figure 5) is used to inform the choice of crop coefficients (Table 2) for the FAO method of calculating a soil balance (Allen et al., 1998). When the soil moisture deficit reduces to zero any additional water is then split between runoff and potential recharge using the runoff coefficient to determine the proportion. Overlaid on this is the river network to which water is routed by the direction of the DEM. Once runoff is generated then it is routed down topographic gradient until it reaches the river where it is routed towards the sea.
Vijayawada is a historical city situated at the geographical centre of Andhra Pradesh state in India on the banks of Krishna River with latitude 160311 N and longitude 800 391 E. Vijayawada now has become the capital of the new state called Andhra Pradesh. There is lot of scope for urban development because of the new state capital construction and there will be severe changes in the landscape of the area. For present study a rectangular area which includes surrounding area of Vijayawada city has been selected. The geographical location study area of Vijayawada is shown in Figure. 1. For collection of field data nearly 100 points were selected over the entire study area from the satellite image. The corresponding coordinates were placed on Google Earth image and using GPS and compass the points were located on the ground during the field visit. Some points were shown on Google earth in the Figure. 2 below.
Ahmedabad is an historical city founded by Sultan Ahmed Shah in 1411 AD on an open plain to the east of river Sabarmati, renowned as a great textile and commercial center and as the ‗Manchester of India‘. It is today a prosperous, thriving city, the second largest in western India with the second largest textile industry in the country . The borough municipality functioned from 1926 and 1950. Urban areas developing very fast lie beyond the corporation limits. These areas come under the Ahmedabad Urban Development Authority (AUDA) which started its functioning from February 1978 under the provision of Gujarat Town Planning and Urban Development Act of 1976 . The area under AUDA is 1294.66 km 2 , which is covered out from four districts, namely Ahmedabad, Mehsana, Kheda and Gandhinagar. The major functioning of the authority is planning and control of development in the area under its jurisdiction and to provide adequate services in the area. In the past three decades population growth was increasing, but in 80‘s it started to decline. This is due to the decline of the textile industry, which was backbone of the city economy . Majority of the household belong to lower middle class category. The income distribution is less skewed which shows higher proportion in low middle and upper middle groups. Population below poverty line is 20-25% and has come down over years.
Nigeria’s Federal Capital City (FCC) is an administrative centre and hosts government offices and international embassies hence, it is rich in infrastructure such as expanding road networks, pipe borne water, drainage and sewage systems, electricity, and communication networks. Due to its central location and accessibility makes people from all parts of the country crowd into the city in search of better living. With an annual growth rate of at least 35%, the city retains its position as the fastest growing city on the African continent and one of the fastest growing in the world. This increasing urban population and massive physical infrastructural development has led to profound changes in the status of its landuse and land cover thereby exposing FCC to increased temperature and heat islands. Although quite a number of studies have been undertaken using satellite-derived LULC data, only a few efforts have been made to access the impact of urban LULC change on the urban thermal environment in Nigeria’s capital city. This study utilizes remote sensing techniques using Landsat images to generate landuse/cover and surface temperature maps in order to evaluate the urbanlanduse/cover changes in FCC, and to analyze the impact of landuse/cover on LST. Landsat spectral bands have proven to be effective not only for identifying LULC (Koutsias & Karteris, 2003), but also for LST change assessment (Loveland & Defries, 2013). This study provides professionals such as landscape architects, environmentalists, and urban planners the much-needed information for putting in place temperature mitigation mechanisms for sustainable city development.
Jaakkola [ 46 ] researched the quality of multiscale land cover data, also using CLC GI, and reported that the generalization process reduces the complexity of the data structure and adds error to the database, therefore the quality is always deteriorated in favor of simplicity and legibility. This author also found errors produced by the generalization of raster GI and refers to the tendency for area decrease for classes covering small areas, while the classes covering large areas with large average feature size tend to suffer an area increase. In fact, these observations have been confirmed by some results obtained in this research, namely the results using CLC. These results, however, differ slightly from the results obtained with COS, where area gain and area loss are very similar using high-resolution raster, without a well-defined trend when the cell size increases. This is mainly due to the GI scale of each LUC dataset, because the COS presents, for a LUC class, more fragmented polygons (due to the MMU), while the CLC is more generalized and presents, naturally, larger polygons.
For the analysis, all available Sentinel-2 Level-1C imagery within a user-defined time frame have been ingested into the workflow. The data itself were obtained from the Copernicus Services Hub. In a later step, information from the single images is translated into monthly composites to reduce the amount of data and to cope with data gaps (due to clouds). To benefit from the wide range of spectral information contained in the Sentinel-2 imagery for change detection, preprocessing of the data is required. The first was an atmospheric correction to minimize atmospheric influences on the observed radiometric response. The Sen2Cor atmospheric correction algorithm was used, which according to the atmospheric correction inter-comparison exercise , shows a similar performance to other atmospheric correction algorithms. Sen2Cor was developed by Telespazio VEGA Deutschland GmbH on behalf of the European Space Agency (ESA). It is a Level-2A processor that corrects single-date Sentinel-2 Level-1C Top-Of-Atmosphere (TOA) products for the effects of the atmosphere to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product. Additional outputs include the Aerosol Optical Thickness (AOT), the Water Vapour (WV) content, and a Scene Classification (SCL) map, with quality indicators for cloud and snow probabilities.
In the previous experiments, we have run ALMA-v1.0 with traders who do not account for the relative power of buyers and sellers, i.e. for epsilon in Equations (6) and (7). Both household- buyers and agricultural sellers thus revealed their true WTP and WTA while submitting bids and asks to the market (see Figures 1 and 2). In the next few experiments, we implement another market behavior: instead of revealing their pure WTP/WTA, agents adjust their bids and asks depending on whether it is a sellers' or a buyers' market-in other words, they become market-oriented. In order to answer the second research question from section 2 above, we analyze macroscopic model outcomes from experiments that implement different pricing strategies at micro level. We increase or decrease the number of buyers in the land market to replicate buyers' or sellers' markets, activating ε in Equation (6) when the number of buyers exceeds the number of sellers, and activating ε in Equation (7) when the number of sellers exceeds the number of buyers. Code verification using the parameter settings for Exp1, but de-activating ε (so that agents submit bids and asks without accounting for the market power of each other) confirmed that unequal numbers of buyers and sellers at the land market did not affect outcomes for homogeneous agents, as expected.
One of the main challenges in spatial planning and de- velopment pattern in the 21st century is urban sprawl. Urban sprawl is defined as a specific form of urban de- velopment with low-density, dispersed, auto-dependent, and environmentally and socially impacting characteris- tics (Hasse and Lathrop 2003). The consequences and negative implications of this type of urban development include increased traffic and demand for mobility (Ewing et al., 2002; Cameron et al., 2004; Kahn, 2000), land-use fragmentation and loss of biodiversity (Alberti, 2005), re- duced attractive landscape (Sullivan and Lovell 2006), and alterations of the hydrological cycle and flooding re- gimes (Bronstert et al., 2002; Carlson, 2004; McCuen, 2003). While today, Metropolitan areas employing smart-growth strategies reap several benefits: the re- gional economy is strengthened, residents ’ quality of life is enhanced, and outer-area natural resource systems are protected and restored (Burchell et al. 1998). Infill devel- opment is a key component of smart growth. Infill development is the process of developing vacant or under-used parcels within existing urban areas that are already largely developed. Most communities have sig- nificant vacant land within city limits, which, for various reasons, has been passed over in the normal course of urbanization. A successful infill development program focuses on the completion of the existing community fabric. It should focus on filling gaps in the existing urban areas (Municipal Research & Services Center of Washington, 1997).
Taking into consideration what was just said, we would like to highlight the following aspects that are important for this analysis and for city planning purposes: i) these plans are designed based on geographic information; ii) this geographic information supports the 2D and 3D representations of cities; and iii) these 2D and 3D representations require fast and low-cost information acquisition methods (Tenedório et al., 2014) taking into consideration the speed at which the city’s functions and morphology change. Theoretically, an urban plan is a formal tool whose aim is the efficiency of a city. Bearing this in mind, two questions might be asked: What is a smart city plan? Is a smart city plan technology-based? The answers to these questions are complex. A smart city corresponds to a vision of urban development that implies the usage of very different technologies. These technologies should promote urban quality of life, i.e., they should aid in the analysis and management of the efficiency of a city’s performance through local departments information systems, e.g., in the fields of energy, transport, water, green spaces, heritage, urban revitalization, urban services in general (education, health, culture, public participation in the discussion of city plans, entertainment and leisure, etc.). In no way can technology be seen as a means of dematerializing urban services; there can be no virtual cities without technology (Tenedório et al., 2014).
During last some decades India has witnessed rapid and uncontrolled urban expansion due to progress in industries, trade and population increase. The anticipation of services and opportunities in cities fuels this growth. When the population increases due to migration, in the outer part of the city, urban sprawl is taking its toll on the natural resources at an alarming place. Land development has been out of control and the construction on land has kept expanding blindly, especially in the marginal areas of these cities. The rural and urban fringe is most rapidly changing element in the urban landscape mapping; landuse/land cover of the rural urban fringe in a timely and accurate manner is thus of great importance for urbanlanduse planning and sustainable management of land resources (Sulochana, 2005).
Cities are on the front line of climate change. Government officials are aggressively targeting cities to reduce energy waste and cut carbon emissions. Today, cities are major consumers of resources and producers of waste having extended their ecological footprints far beyond their official borders. A secure plan for future global development will require cities to evolve into more sustainable ecosystems (Lenzen and Peters, 2010; Næss, 2001). However, due to their large size, socioeconomic structures and geopolitical attributes the patterns of change in cities are very complex (Hall, 1998). A comprehensive analysis of the dynamic of urban resource flows is critical to understand and address ecological challenges in the path towards a sustainable urbanized planet (Akimoto et al., 2008; Vera and Langlois, 2007). In this context, urban planning researchers have made great strides in developing methods to understand and model resource usage among different demographic populations (P_erez-Lombard et al., 2008). This knowledge base has extended to quantify how building type, location, and clustering impacts urban flows (Ratti et al., 2005). This paper describes the framework for an integrated urban metabolism analysis tool (IUMAT) to enable policymakers to assess the impact of changes to demographics, economics, land cover, transportation, energy and water and material resources. IUMAT is expected to promote greater understanding about the impact of environmental policies and development strategies at an urban scale, focusing on areas where sustainable urban planning and growth are critical to climate change mitigation and greenhouse gas reduction.
Urban temperature rise and formation of urban heat island (UHI) has long been a concern for more than 60 years. One of the earliest UHI studies was conducted in 1964 by Nieuwolt (1966) in the urban southern Singapore. Afterward, host of scientist (Zhong, 1996; Deosthali, 2000; Kim and Baik, 2002; Giridharan et al., 2004; Weng, 2009; Neteler, 2010; Ogashawara and Brum Bastos, 2012; Grover and Singh, 2015) have worked on this field emphasizing different cognitive issues. UHI intensity is related to patterns of landuse/cover changes (LUCC), e.g. the composition of vegetation, water and built-up and their changes (Chen et al., 2006; Grover and Singh, 2015). Both horizontal and vertical urban expansion, spacing between building, building materials, location of public places, bus stoppage, railway station, major and minor industrial hubs etc. influence temperature concentration. Consid- ering the importance of the work, many such scholarly works were done in the foreign nations. Cao et al. (2008) applied Fractal vege- tation cover (FVC) change while detecting urban heat island and its shifting behaviour. Jiang and Tian (2010) used temperature-
19 reference, there are clearly deviations in the predicted inundation extent and depth under different simulations. The impact in terms of inundation extent and depth is generally proportionate to the rate of land subsidence throughout the simulations for each period. The sensitivity and response of both measures to land subsidence can be readily distinguished. However, the temporal differences in F statistics between 2006 and 2011 simulations are less marked due to the relatively slow subsidence rates (~5 mm/year) during the period. It is also noted that for the depth RMSD curves, the impact of land subsidence between 2005 and 2010 is predicted to be less than that for 2000 to 2005. The findings support those in section 3.2 and further suggest that in addition to the rate of land subsidence, the spatial variation in relative topographic relief at a local scale affects the distribution of flooded extent and depth. Although land subsidence continues, the spatial variation diminishes up to 2006, when the rate of land subsidence begins to stabilize. Topographic variations within the modelled domain become less marked and hence the funnel area is less discernible over time, potentially alleviating the adverse impact arising from land subsidence. Given that the chosen watershed is one of the most severely affected areas by land subsidence in Shanghai, it is expected that similar findings will also hold for other watersheds in the city. However, depending on the relative rate of land subsidence across a watershed, other regions might exhibit dissimilar response patterns.
During my time as CAPES Chair in WWU, I lead a research team that worked to test the capacity of the SciDB software to handle big geospatial data. To assess its performance, we carried out a benchmark using the MODIS09 land product. Each MODIS09 tile covers 4800 x 4800 pixels with three bands at 250 meters ground resolution. We combined more than ten years of data (544 time steps) of the 22 MODIS images covering Brazil. A total of 11,968 images were merged into an array of 2.75x10 11 (275 billion) cells and loaded into SciDB. We tested different SciDB operations on this dataset. The subarray operation selects subsets of large arrays. The apply function allows the application of a function to all elements of an array. The filter operation selects from an array those cells that match a predicate. The aggregate function calculates a combined value (e.g., the average) for all elements of an array. Fig. 3 shows timings of these operations as a function of array size for an average of 5 runs for cells varying in size from 46*1024 2 to 46*14336 2 . The results show an