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

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)

Monitoring and modeling urban sprinkling: a new perspective of land take

According to the studies done until now on the recent urban transformation dynamics, namely urban sprinkling, this thesis aims to investigate the phenomenon from different points of view to bring out its unsustainable character. The urban dispersion phenomena, specific characteristic of low-density territories, will be examined through the sprinkling index by including new components in addition to the traditional settlement system components. It allows to evaluate the shape of the anthropic settlements and the distance between them which often results in fragmentation of the urban settlements which in turn generate landscape fragmentation. Nowadays, both in the proximity of large cities and in more external areas such as rural areas, there are often evidences of strong fragmentation of the anthropic settlements in which, even if the amount of occupied surface (land take) may not seem worrying, its configuration determines a general decrease in ecological connectivity, landscape quality and general degradation of soil functions. The general hypothesis is that fragmentation (of urban, landscape and habitat) can become an indicator of land take. In fact, it is not enough to consider only the loss of natural or agricultural areas, but also the distribution of buildings in the landscape matrix, i.e., its spatial component. An emblematic case is that of Basilicata region whose dynamics of transformation from the 50s to the present day will be investigated in this thesis. According to the latest report of the Italian Institute for Environmental Protection and Research (ISPRA 2020), the Basilicata region has only 3.15% of land consumption compared to the entire regional surface. This indicator is in contrast with the shape of the anthropic settlements which results fragmented and dispersed. It is essential that the effects of fragmentation as well as ecosystem disaggregation take on a "measurable" character, joining the list of indicators of urban and territorial quality such as land take and land consumption that European Union addresses to national communities currently consider essential and decisive to highlighting the efficiency/inefficiency of environmental and landscape management. It is crucial to understand and investigate what have been and will be in the future the most influential drivers on these dynamics that contribute intrinsically to land consumption and to define the addresses or the thresholds to contain this pulverized and disordered dissemination of anthropic settlements

Predicting land use changes in northern China using logistic regression, cellular automata, and a Markov model

Abstract(#br)Land use changes are complex processes affected by both natural and human-induced driving factors. This research is focused on simulating land use changes in southern Shenyang in northern China using an integration of logistic regression, cellular automata, and a Markov model and the use of fine resolution land use data to assess potential environmental impacts and provide a scientific basis for environmental management. A set of environmental and socio-economic driving factors was used to generate transition potential maps for land use change simulations in 2010 and 2020 using logistic regression. An average relative operating characteristic value of 0.824 was obtained, indicating the validity of the logistic regression model. The logistic–cellular automata (CA)–Markov model..

Linnade laienemine Eestis: seire, analüüs ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLinnade laienemine, mida iseloomustab vähese tihedusega, ruumiliselt ebaühtlane ja hajutatud areng linna piiridest välja. Kuna linnade laienemine muudab põllumajandus- ja metsamaid ning väikesed muutused linnapiirkondades võivad pikaajaliselt mõjutada elurikkust ja maastikku, on hädavajalik seirata linnade ruumilist laienemist ning modelleerida tulevikku, saamaks ülevaadet suundumustest ja tagajärgedest pikemas perspektiivis. Eestis võeti pärast taasiseseisvumist 1991. aastal vastu maareformi seadus ning algas “maa” üleandmine riigilt eraomandisse. Sellest ajast peale on Eestis toimunud elamupiirkondade detsentraliseerimine, mis on mõjutanud Tallinna ümbruse põllumajandus- ja tööstuspiirkondade muutumist, inimeste elustiili muutusi ning jõukate inimeste elama asumist ühepereelamutesse Tallinna, Tartu ja Pärnu lähiümbruse. Selle aja jooksul on Eesti rahvaarv vähenenud 15,31%. Käesoleva doktoritöö eesmärgiks on "jälgida, analüüsida ja modelleerida Eesti linnade laienemist viimase 30 aasta jooksul ning modelleerida selle tulevikku", kasutades paljusid modelleerimismeetodeid, sealhulgas logistilist regressiooni, mitmekihilisi pertseptronnärvivõrke, rakkautomaate, Markovi ahelate analüüsi, mitme kriteeriumi. hindamist ja analüütilise hierarhia protsesse. Töö põhineb neljal originaalartiklil, milles uuriti linnade laienemist Eestis. Tegu on esimese põhjaliku uuringuga Eesti linnade laienemise modelleerimisel, kasutades erinevaid kaugseireandmeid, mõjutegureid, parameetreid ning modelleerimismeetodeid. Kokkuvõtteks võib öelda, et uusehitiste hajumismustrid laienevad jätkuvalt suuremate linnade ja olemasolevate elamupiirkondade läheduses ning põhimaanteede ümber.Urban expansion is characterized by the low–density, spatially discontinued, and scattered development of urban-related constructions beyond the city boundaries. Since urban expansion changes the agricultural and forest lands, and slight changes in urban areas can affect biodiversity and landscape on a regional scale in the long-term, spatiotemporal monitoring of urban expansion and modeling of the future are essential to provide insights into the long-term trends and consequences. In Estonia, after the regaining independence in 1991, the Land Reform Act was passed, and the transfer of “land” from the state to private ownership began. Since then, Estonia has experienced the decentralization of residential areas affecting the transformation of agricultural and industrial regions around Tallinn, changes in people's lifestyles, and the settling of wealthy people in single-family houses in the suburbs of Tallinn, Tartu, and Pärnu. During this period, Estonia's population has declined dramatically by 15.31%. Therefore, this dissertation aims to "monitor, analyze and model Estonian urban expansion over the last 30 years and simulate its future" using many modeling approaches including logistic regression, multi-layer perceptron neural networks, cellular automata, Markov chain Analysis, multi-criteria evaluation, and analytic hierarchy process. The thesis comprises four original research articles that studied urban expansion in Estonia. So far, this is the first comprehensive study of modeling Estonian urban expansion utilizing various sets of remotely sensed data, driving forces and predictors, and modeling approaches. The scattering patterns of new constructions are expected to continue as the infilling form, proximate to main cities and existing residential areas and taking advantage of main roads in future.https://www.ester.ee/record=b550782

Beyond the urban-rural dichotomy:Towards a more nuanced analysis of changes in built-up land

Urban land and rural land are typically represented as homogenous and mutually exclusive classes in land change analyses. As a result, differences in urban land use intensity, as well as mosaic landscapes combining urban and rural land uses are not represented. In this study we explore the distribution of urban land and urban land use intensity in Europe and the changes therein. Specifically, we analyze the distribution of built-up land within pixels of 1 km2. At that resolution we find that most built-up land is distributed over predominantly non-built-up pixels. Consistently, we find that most urban land use changes between 2000 and 2014 come in small incremental changes, rather than sudden large-scale conversions from rural to urban land. Using urban population densities, we find that urban land use intensity varies strongly across 1 km2 pixels in Europe, as illustrated by a coefficient of variation of 85%. We found a similarly high variation between urban population densities for most individual countries and within areas with the same share of built-up land. Population changes have led to different combinations of urban land expansion and urban intensity changes in different study periods (1975–1990, 1990–2000, and 2000–2015) and countries. These findings suggest that land use change models could be improved by more nuanced representations of urban land, including mosaic classes and different urban land use intensities

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future

Challenges and prospects of spatial machine learning

The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.Das wesentliche Ziel dieser Arbeit ist es, die Nützlichkeit des räumlichen maschinellen Lernens für die Raumwissenschaften zu verbessern und es zu ermöglichen, ungenutztes Potenzial auszuschöpfen. Um dieses Ziel zu erreichen, befasst sich diese Arbeit mit mehreren wichtigen Herausforderungen, denen das räumliche maschinelle Lernen gegenübersteht. Diese sind die Modellierung von räumlicher Autokorrelation und räumlicher Heterogenität, die Auswahl eines geeigneten Modells für ein gegebenes räumliches Problem und das Verständnis komplexer räumlicher maschineller Lernmodelle