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

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

This paper presents a model to simulate built-up expansion and densification based on a combination of a non-ordered multinomial logistic regression (MLR) and cellular automata (CA). The probability for built-up development is assessed based on (i) a set of built-up development causative factors and (ii) the land-use of neighboring cells. The model considers four built-up classes: non built-up, low-density, medium-density and high-density built-up. Unlike the most commonly used built-up/urban models which simulate built-up expansion, our approach considers expansion and the potential for densification within already built-up areas when their present density allows it. The model is built, calibrated, and validated for Wallonia region (Belgium) using cadastral data. Three 100 × 100 m raster-based built-up maps for 1990, 2000, and 2010 are developed to define one calibration interval (1990–2000) and one validation interval (2000 − 2010). The causative factors are calibrated using MLR whereas the CA neighboring effects are calibrated based on a multi-objective genetic algorithm. The calibrated model is applied to simulate the built-up pattern in 2010. The simulated map in 2010 is used to evaluate the model’s performance against the actual 2010 map by means of fuzzy set theory. According to the findings, land-use policy, slope, and distance to roads are the most important determinants of the expansion process. The densification process is mainly driven by zoning, slope, distance to different roads and richness index. The results also show that the densification generally occurs where there are dense neighbors whereas areas with lower densities retain their densities over time

Combining a land parcel cellular automata (LP-CA) model with participatory approaches in the simulation of disruptive future scenarios of urban land use change

Urban development is a process that becomes increasingly complex as the city evolves and in which unexpected events can happen which may alter the envisaged trend over time. To anticipate and examine the sudden emergence of processes that are difficult to predict over long-term future timelines, prospective methodologies are required to manage and implement disruptive narrative storylines in future scenario planning. In this research, a method that combines Land Parcel Cellular Automata (LP-CA) and participatory approaches was developed in order to generate land use trajectories that are spatially consistent with disruptive narrative storylines. The urban-industrial corridor of Henares (Spain), which has undergone important urban transformations in recent decades, was chosen as the study area to test the model. In a preliminary validation of the LP-CA model, a Figure of Merit (FOM) value of 0.2817 indicated satisfactory performance. The results demonstrated the usefulness of the participatory scenario-building and the workshop in supporting the configuration of the model parameters and the spatial representation of complex urban dynamics. In conclusion, this methodology can be used to generate simulations of urban land use change in disruptive future scenarios and to spatially observe the propagation of the uncertainty associated with future events across different urban land uses.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Social Fund [grant number PRE2018–084663]; the Spanish Ministry of Economy and Competitiveness [TRANSURBAN Project CSO2017–86914-C2–1-P]; and the “Estímulo a la Excelencia para Profesores Universitarios Permanentes” research programme funded by the University of Alcal´a and the Regional Government of Madrid [grant number EPU-INV/2020/009]

Urban growth models and calibration methods: a case study of Athens, Greece

A number of urban growth models have been developed to simulate and predict urban expansion. Most of these models have common objectives; however, they differ in terms of calibration and execution methodologies. GIS spatial computations and data processing capabilities have given us the ability to draw more effective simulation results for increasingly complex scenarios. In this paper, we apply and evaluate a methodology to create a hybrid cellular-automaton- (CA) and agent-based model (ABM) using raster and vector data from the Urban Atlas project as well as other open data sources. We also present and evaluate three different methods to calibrate and evaluate the model. The model has been applied and evaluated by a case study on the city of Athens, Greece. However, it has been designed and developed with the aim of being applicable to any city available in the Urban Atlas project

Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.Spanish GovernmentEuropean Commission INCERTIMAPS PGC2018-100770-B-100Spanish Ministry of Economy and Competitiveness and the European Social Fund [Ayudas para contratos predoctorales para la formacion de doctores 2014]University of Granada [Contratos Puente 2018]Spanish Ministry of Science and Innovation [Ayudas para contratos Juan de la Cierva-for-macion] 2019-FJC2019-040043University of Cape Town (Centre for Transport Studies

The Incidence of Land Use Regulations

I study the welfare consequences of land use regulations for low- and high-skilled workers within a city. I use detailed geographic data for Cook County and Chicago in 2015-2016, together with a spatial quantitative model with two types of workers and real estate developers who face regulations. For identification, I use the 1923 Zoning Ordinance, which was the first comprehensive ordinance in Chicago. I find that an increase of 10 percentage points in the share of residential zoning in a block group, relative to block groups with more commercial zoning, leads to a 1.7% increase in housing prices, a 2.6% decrease in wages and a higher concentration of high-skilled residents. Welfare changes can be decomposed into changes in housing prices, sorting, wages and land rents. Results suggest that more mixed-use zoning and looser floor-to- area limits lead to welfare improvements, especially for low-skilled residents, and to a reduction in welfare inequality

Spatiotemporal modeling of interactions between urbanization and flood risk: a multi-level approach

The main goal of this PhD research is to investigate the expected flood damage for future urban patterns at different scales. Four main steps are followed to accomplish this goal. In the first step, a retrospective analysis is performed for the evolution of the urban development in Wallonia (Belgium) as a case study. Afterward, two land use change models, cellular automata-based, and agent-based are proposed and compared. Based on this comparison, the agent-based model is employed to simulate future urbanization scenarios. An important feature of this research is evident in the consideration of the multiple densities of built-up areas, which enables to study both expansion and densification processes. As the model simulates urbanization up to 2100, forecasting land use change over such time frames entails very significant uncertainties. In this regard, uncertainty in land use change models has been considered. In the third step, 24 urbanization scenarios that differed in terms of spatial policies and urbanization rate are generated. The simulated scenarios have then been integrated with a hydrological model. The results suggest that urban development will continue within flood-prone zones in a number of scenarios. Therefore, in the fourth and last step, a procedural urban generation system is developed to analyze the respective influence of various urban layout characteristics on inundation flow, which assists in designing flood-resistant urban layouts within the flood-prone zones.This thesis was funded through the ARC grant for Concerted Research Actions for project number 13/17-01 entitled "Land-use change and future flood risk: influence of micro-scale spatial patterns (FloodLand)" financed by the French Community of Belgium (Wallonia-Brussels Federation)