Configuring the neighbourhood effect in irregular cellular automata based models

Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models

Estimating spatial accessibility to facilities on the regional scale: an extended commuting-based interaction potential model

<p>Abstract</p> <p>Background</p> <p>There is growing interest in the study of the relationships between individual health-related behaviours (e.g. food intake and physical activity) and measurements of spatial accessibility to the associated facilities (e.g. food outlets and sport facilities). The aim of this study is to propose measurements of spatial accessibility to facilities on the regional scale, using aggregated data. We first used a potential accessibility model that partly makes it possible to overcome the limitations of the most frequently used indices such as the count of opportunities within a given neighbourhood. We then propose an extended model in order to take into account both home and work-based accessibility for a commuting population.</p> <p>Results</p> <p>Potential accessibility estimation provides a very different picture of the accessibility levels experienced by the population than the more classical "number of opportunities per census tract" index. The extended model for commuters increases the overall accessibility levels but this increase differs according to the urbanisation level. Strongest increases are observed in some rural municipalities with initial low accessibility levels. Distance to major urban poles seems to play an essential role.</p> <p>Conclusions</p> <p>Accessibility is a multi-dimensional concept that should integrate some aspects of travel behaviour. Our work supports the evidence that the choice of appropriate accessibility indices including both residential and non-residential environmental features is necessary. Such models have potential implications for providing relevant information to policy-makers in the field of public health.</p

Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values

This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR) enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural) attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters

Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor

In this study, we link and compare the geographically weighted regression (GWR) model with the kriging with an external drift (KED) model of geostatistics. This includes empirical work where models are performance tested with respect to prediction and prediction uncertainty accuracy. In basic forms, GWR and KED (specified with local neighbourhoods) both cater for nonstationary correlations (i.e. the process is heteroskedastic with respect to relationships between the variable of interest and its covariates) and as such, can predict more accurately than models that do not. Furthermore, on specification of an additional heteroskedastic term to the same models (now with respect to a process variance), locallyaccurate measures of prediction uncertainty can result. These heteroskedastic extensions of GWR and KED can be preferred to basic constructions, whose measures of prediction uncertainty are only ever likely to be globallyaccurate. We evaluate both basic and heteroskedastic GWR and KED models using a case study data set, where data relationships are known to vary across space. Here GWR performs well with respect to the more involved KED model and as such, GWR is considered a viable alternative to the more established model in this particular comparison. Our study adds to a growing body of empirical evidence that GWR can be a worthy predictor; complementing its more usual guise as an exploratory technique for investigating relationships in multivariate spatial data sets

A microsimulation approach for modelling the growth of small urban areas

Tese de mestrado. Projecto e Planeamento do Ambiente Urbano. Faculdade de Engenharia. Universidade do Porto, Universidade de Coimbra. Faculdade de Ciências e Tecnologia. 200

The dynamic interaction of land use and transport in a highly fragmented city: the case of Cape Town, South Africa

The need for more inclusive and integrated cities has resulted in a paradigm shift in the South African transport and land use policy environment where transport and land use planning are viewed as a continuum as opposed to isolated planning aspects. Issues such as residential segregation, social exclusion, spatial inefficiencies, inequality, residential informality, marginalisation of the low-income cohort continue to form part of the current planning discourse. While policy acknowledges the need to redress these issues, the urban spatial patterns in South African cities continue to trace the historical planning trajectory. Recently, congestion has become an issue in some of South Africa’s cities with Johannesburg and Cape Town appearing in the list of the top hundred most congested cities in the world. It is thus essential to understand how South African cities can address urban accessibility and mobility issues along with redressing apartheid spatial planning to attain sustainable cities that allow for inclusivity of all population groups. Like most South African cities, Cape Town is a relic of apartheid planning where the urban spatial patterns reinforce social exclusion among other issues. Urban and transport planning in Cape Town focuses on addressing issues of spatial inefficiencies, social exclusion, congestion due to rapid motorisation and the proliferation of informal settlements. It is against this backdrop that the central concern of this research is to understand urban dynamics linked to the spatiotemporal interaction of transport and land use in Cape Town to aid in the formulation of proactive urban policies. There is compelling evidence in the literature that dynamic integrated land use transport models provide an avenue through which the urban change process can be understood to aid in the development of adaptive land use and transport strategies. METRONAMICA, a dynamic land use transport model, is applied in this research to simulate and understand land use and transport change in Cape Town. A sequential stage-wise procedure was implemented to calibrate the model for the period 1995- 2005 and an independent validation was carried out from 2005 to 2010 to evaluate the model. Kappa statistic and its associated variants were applied to assess the ability of the land use model block to reproduce land use patterns while the EMME model and previous transport studies for Cape Town were used to evaluate the transport model. The results from the calibration and validation exercise show that the model can reproduce historical land use and transport patterns. The integration of the transport and land use model through accessibility improved the Kappa Simulation and Fuzzy Kappa Simulation. This showed that the model explained urban change better when land use and transport interacted compared to an independent land use model. This shows that accessibility can be employed in the Cape Town context to enhance the understanding of the urban change process. In addition to the Kappa statistics, the fractal dimension which measures the landscape complexity was used to assess the predictive accuracy of the model. The model performance revealed that the landscape patterns simulated by the model resemble observed land use patterns signifying a good calibration of the model. The calibrated land use transport model for the Cape Town Metropolitan region (CTMRLUT) was applied for policy scenarios. Three scenarios were simulated, specifically the business as usual (BAU), redressing social exclusion and the potential for in situ upgrading of informal settlements. The study found that intensive land use development along the Metro South East Integration Zone (MSEIZ) was linked to a reduction in commuting distances to economic activities which is in contrast to the BAU scenario. While these scenarios looked at the urban spatial patterns, the effect of land use patterns on congestion was also explored. The findings from the scenario simulations suggest that despite the reduction in distance to economic centres, the congestion condition in Cape Town will continue to deteriorate. Further, the findings indicate that interventions that only target land use developments are not sufficient to address congestion issues in Cape Town. Instead, to address the congestion problem in Cape Town, mixed land use and compact growth strategies need to be complemented with travel demand management strategies that target private car usage and intensive investment in transport infrastructure, especially rail, to facilitate the use of alternative modes. With regards to informal settlements, the study found that in situ upgrading could be a viable option to tackle some informal settlements. However, for proper inclusionary informal settlement policy, an approach that resonates with contextual realities would be more suitable to assess the viability of in situ upgrading based on the location of informal settlements relative to centres of economic activities. Additionally, the study revealed that instead of informal settlements locating as stand-alone settlements, some of them located adjacent to low-income housing which might be indicative of a growth in backyard shacks which is an existing housing trend in some lowincome suburbs in Cape Town. While this research has shown that integrating land use and transport in policy is potentially useful in solving urban issues, it has also revealed the value of urban modelling as a platform on which to assess the potential impacts of policies before their implementation. This is a strong case for the utilisation of decision support tools in land use and transport planning in contemporary South African cities

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management

Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change

Spatial Optimization of Urban Cellular Automata Model

Although cellular automata (CA) offer a modelling framework and set of techniques for modelling the dynamic processes of urban growth, determining the optimal value of weights or parameters for elements or factors of urban CA models is challenging. This chapter demonstrates the implementation of a calibration module in a fuzzy cellular urban growth model (FCUGM) for optimizing the weights and parameters of an urban CA model using three types of algorithms: (i) genetic algorithm (GA), (ii) parallel simulated annealing (PSA) and (iii) expert knowledge (EK). It was found that the GA followed by EK produced better and more accurate and consistent results compared with PSA. This suggests that the GA was able to some extent to understand the urban growth process and the underlying relationship between input factors in a way similar to human experts. It also suggests that the two algorithms (GA and EK) have similar agreement about the efficiency of scenarios in terms of modelling urban growth. In contrast, the results of the PSA do not show results corresponding to those of the GA or EK. This suggests that the complexity of the urban process is beyond the algorithm’s capability or could be due to being trapped in local optima. With this satisfactory calibration of the FCUGM for the urban growth of Riyadh city in Saudi Arabia by using CALIB-FCUGM, these calibrated parameters can be passed into the SIM-FCUGM to simulate the spatial patterns of urban growth of Riyadh

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%