Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

Exploring the Driving Forces of Urban Expansion in Beijing, 1999-2013

This research focuses on analyzing the spatial patterns and driving forces of built-up expansion in Beijing during 1999-2013. During this period, land leasing by the local government and the decentralization strategy in urban planning facilitates land development on the urban fringe. In order to investigate the spatial patterns of the expansion of urban built-up area and to unfold the relationship between non-urban to urban conversion and the selected explanatory factors, remote sensing data, GIS and statistic tools are utilized. First, sector and concentric circle analysis reveals that within the study time frame, Beijing extends greatly to the northeast and southeast, which is driven by the economic growth opportunities in these areas, especially the designation of CBD and Development Zones. Second, this research uses the classic logistic regression and the Geographically Weighted Logistic Regression (GWLR) model as statistic tools to further explain the relations between urban expansion and factors from three categories, natural environment, proximity to centers and transportation, and socioeconomic status. According to the results, the distance to CBD, distance to sub-centers, and distance to main roads are proved to be influential factors during the study time frame. The regression results also unveil that the population growth is in line with the urban expansion, indicating the interactions between population dynamics and the change of urban form. In addition, spatially varying relationships are examined through mapping the local parameter estimates of the GWLR model. Each factor has various significance and parameter estimates over space and different urban development modes are found in different areas of the city

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Modélisation spatiale des changements dans les milieux humides ouverts par automate cellulaire : étude de cas sur la région administrative de l’Abitibi-Témiscamingue, au Québec, Canada

Les milieux humides sont parmi les écosystèmes les plus productifs qui existent à travers la planète. Trente-cinq pour cent des zones humides du monde se trouvent au Canada, avec un quatre-vingt-cinq pour cent environ situés dans la forêt boréale. Cependant, ces écosystèmes sont parmi les plus menacés en raison des perturbations humaines. Malheureusement, une fois qu’un milieu humide a perturbé, il est difficile de le ramener à son état naturel. L’étude de la complexité autour des dynamiques de changement dans les milieux humides peut être employée par l’utilisation d’outils de modélisation et de simulation spatiotemporelle pour aider la conservation de l’environnement. Les approches de modélisation de systèmes complexes telles que les automates cellulaires combinés à des modèles statistiques nous permettent de simplifier ces complexités et de comprendre les modèles émergents de systèmes complexes, tels que les milieux humides. Cette étude propose la simulation des milieux humides ouverts basée sur le modèle hybride par régression logistique, chaîne Markov et automates cellulaires, afin de projeter des scénarios futurs de la distribution des milieux humides ouverts dans la région administrative de l’Abitibi-Témiscamingue, Québec. Ce mémoire comprend deux parties : 1) le diagnostic des zones humides et la caractérisation de la zone d’étude; et 2) un article sur la modélisation spatiotemporelle des changements dans les milieux humides ouverts en utilisant le modèle hybride, afin de simuler leur distribution spatiale pour les années 2015, 2025, 2035, 2045 et 2055 dans la région administrative de l’Abitibi-Témiscamingue. Les résultats de la simulation ont montré une augmentation moyenne de cinq pour cent entre les simulations de 2015 et 2055. Les résultats sont en accord avec les modèles spatiotemporels observés à partir des images Landsat de 1985, 1995 et 2005. La distribution spatiale observée et projetée des milieux humides ouverts dans la région étudiée offre un aperçu de la dynamique de cet écosystème fragile. Avec l’augmentation des milieux humides ouverts, la disponibilité de l’habitat pour la sauvagine augmentera aussi, en plus les services qui y sont associés. Les résultats de cette recherche apportent de nouvelles informations et perspectives en termes de futures politiques de conservation des milieux humides ouverts.Wetlands are among the most productive ecosystems that exist throughout the planet. Thirty five per cent of the world’s wetlands can be found in Canada, with an approximately eighty five percent located in the boreal forest However, these ecosystems are among the most threatened ecosystems due human disturbances. Regrettably, once a wetland has been disturbed it is difficult to restore it to its natural state. The study of the complexities around dynamic changes in wetlands can be approached by the use of modeling and spatiotemporal simulations as tools for assisting environmental conservation. Complex systems modeling approaches such as cellular automata coupled with statistical models allow us to simplify these complexities and understanding emerging patterns of complex systems, such as wetlands. This study proposes the simulation of open wetlands based on a hybrid model by logistic regression, Markov chain and cellular automata, in order to project future scenarios of open wetlands distribution in the administrative region of Abitibi-Témiscamingue, Quebec. This thesis consists of two parts: 1) wetland diagnosis and characterization of the study area; and 2) an article on the modeling of spatiotemporal changes in open wetlands using a hybrid model, to simulate spatial distribution of open wetlands for the years 2015, 2025, 2035, 2045 and 2055 in the Abitibi-Témiscamingue administrative region. Model simulation results showed an average increment, by decade, of over five percent between simulations from 2015 to 2055. The results agreed with the observed spatiotemporal patterns from Landsat imagery from 1985, 1995, and 2005. The observed and projected spatial distribution of open wetlands in the study region offer some insight of the dynamics of this fragile ecosystem. With an increase in open wetlands, habitat availability for waterfowl will as well augment, in addition to the services associated with them. The outcomes of this research bring new information and perspectives in terms of future open wetlands conservation policies

Urban expansion modeling using machine learning algorithms

Modeling and simulating urban expansion is required for assessing and predicting the consequences of the current urban growth patterns. Given the dynamic and convoluted nature of the urban expansion process and the necessity of handling continuous and categorical variables, non-normal distributed data, and non-linear relationships, urban expansion modeling is challenging. It is also critically important to find an appropriate method for modeling and simulating urban expansion in order to meticulously identify spatiotemporal variables and predicting the direction of land use/land cover (LULC) changes. To handle these issues effectively and enhance the quality of urban expansion prediction, the capabilities of machine learning methods are explored in this dissertation. Machine learning methods are relatively unknown in urban expansion modeling and have not been evaluated thoroughly in the current literature. The machine learning methods allow the exploration of a variety of data sampling strategies, predictor variables, and model configurations to enhance the accuracy and predictability of urban expansion modeling. The models are calibrated using spatiotemporal data of 2001-2016 and are applied to simulate future urban developments for two urbanized counties—Guilford and Mecklenburg in NC, USA. The accuracy and reliability of the models are evaluated by apposite evaluation metrics. Distance to highways is recognized as the most important predictor variable in both study areas, however, the importance of the predictor variables varies in different geographic contexts and with different methods. A comparative study on machine learning methods demonstrated that the random forest (RF) model is a fast, high-performance, and accurate model with low uncertainty; therefore, it can be effectively utilized to evaluate a wide range of urban development scenarios and support decision-making to accomplish the goal of implementing environmentally sustainable development. Sustainable urban growth management in addition to sophisticated and elaborative models requires different urban growth scenarios. An integration of random forest and cellular automata (RF-CA) is proposed to simulate urban development under three urban growth scenarios, including current trends, controlled urban development, and environmentally sustainable urban development. While current trends allow the urban fringe to be uncontrollably developed, the controlled and environmentally sustainable urban development scenarios constrain future developments and reduce the environmental implications. The results show that the current urban development in the study area for 2021 and 2026 will appear near current or newly built urban clusters or adjacent to the major roads, however, the controlled and environmentally sustainable urban development scenarios are much higher compact and minimize environmental costs

Dynamic land use/cover change modelling

Landnutzungswandel ist eine komplexe Angelegenheit, die durch zahlreiche biophysikalische, sozioökonomische und wirtschaftliche Faktoren verursacht wird. Eine offensichtliche Art des Landnutzungswandels, die in den suburbanen Gebieten einer Metropole stattfindet, ist die Zersiedelung. Es gibt viele Modellierungstechniken, um dieses Phänomen zu studieren. Diese wurden seit den 1960iger Jahren entwickelt und finden weite Verbreitung. Einige dieser Modelle leiden unter dem Vernachlässigen signifikanter Variablen. Traditionelle Methoden wie etwa zellulare Automaten, Markow-Ketten-Modelle, zellulare Automaten-Markow-Modelle und logistische Regressionsmodelle, weisen inhärente Schwächen auf in Bezug auf menschliche Aktivitäten in der Umwelt. Das liegt daran, dass der Mensch der Hauptakteur in der Transformation der Umwelt ist und die suburbanen Gebiete durch Niederlassungspräferenzen und Lebensstil prägt. Das Hauptziel dieser Dissertation ist es, einige dieser traditionellen Techniken zu untersuchen, um ihre Vor- und Nachteile zu identifizieren. Diese Modelle werden miteinander verglichen, um ihre Funktionalität zu hinterfragen. Obwohl die Methodologie zur Evaluierung agentenbasierter Modelle unzureichend ist, wurde hier versucht, ein selbst-kalibriertes agentenbasiertes Modell für den Großraum Teheran zu erstellen. Einige Variablen, die in der Wirklichkeit die Zersiedelung im Studiengebiet kontrollieren, wurden durch Expertenwissen und ähnliche Studien extrahiert. Drei Hauptagenten, die mit der Ausbreitung von Städten zu tun haben, wurden definiert: Entwickler, Bewohner, Behörden. Jeder einzelne Agent beeinflusst Variablen; d.h. die Entscheidungen eines Agenten werden von einer Reihe realer Variablen beeinflusst. Das Verhalten der einzelnen Agenten wurde in einer GIS Umgebung kodiert und anschließend zusammengeführt, um einen Prototyp zur Simulation der Landnutzungsänderung zu erzeugen. Dieser Geosimulations-Prototyp ist in der Lage, die Quantität und die Lage von Landnutzungsänderungen insbesondere in der Umgebung von Teheran zu simulieren. Dieses agentenbasierte Modell zieht Nutzen aus der Stärke traditioneller Techniken wie etwa zellularen Automaten zur Änderungsallokation, Markow-Modellen zur Schätzung der Quantität der Änderung und einer Gewichtung der individuellen Faktoren. Eine detaillierte Diskussion der Implementierung der unterschiedlichen Methoden sowie eine Stärken-Schwächen-Analyse werden präsentiert und die Ergebnisse mit der tatsächlichen Situation verglichen, um die Modelle zu verifizieren. In dieser Arbeit wurden GIS Funktionen verwendet und zusätzliche Funktionen in Python programmiert. Diese Untersuchungen sollen Stadtplaner und Entscheidungsträger unterstützen, Städte und deren Ausbreitung zu simulieren.Land use/ cover change is a complex matter, which is caused by numerous biophysical, socio-economical and economic factors. An obvious form of land use change in the suburbs of the metropolis is defined as urban sprawl. There are a number of techniques to model this issue in order to investigate this topic. These models have been developed since the 1960s and are increasing in terms of quantity and popularity. Some of these models suffer from a lack of consideration of some significant variables. The traditional methods (e.g. Cellular Automata, the Markov Chain Model, the CA-Markov Model, and the Logistic Regression Model) have some inherent weaknesses in consideration of human activity in the environment. The particular significance of this problem is the fact that humans are the main actors in the transformation of the environment, and impact upon the suburbs due to their settlement preferences and lifestyle choices. The main aim of this thesis was to examine some of those traditional techniques in order to discover their considerable advantages and disadvantages. These models were compared against each other to challenge their functionality. Whereas there is a lack of methodology in evaluation of agent-based models, it was presumed to create a self-calibrated agent based model, by focussing on the Tehran metropolitan area. Some variables in reality control urban sprawl in the study area, which were extracted through the expert knowledge and similar studies. Three main agents, which deal with urban expansion, were defined: developers, residents, government. Each particular agent affects some variables, i.e. the agents‟ decisions are being influenced by a set of real variables. Agents‟ behaviours were coded in a GIS environment and, thereafter, the predefined agents were combined through a function to create a prototype for simulation of land change. This designed geosimulation prototype can simulate the quantity and location of changes specifically in the vicinity of the metropolis of Tehran. This customised agent-based model benefits from the strengths of traditional techniques; for instance, a Cellular Automata structure for change allocation, a Markov model for change quantity estimation and a weighting system to differentiate between the weights of the driving factors. A detailed discussion of each methodology implementation, and their weakness and strengths, is then presented, specifically comparing results with the reality to verify the models. In this research, we used only the GIS functionalities within GIS environments and the required functions were coded in the Python engine. This investigation will help urban planners and urban decision-makers to simulate cities and their movements over time

Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)

Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management

An Integrated Remote Sensing and Urban Growth Model Approach to Curb Slum Formation in Lagos Megacity

Rapid urbanization with limited development has led to slum proliferation in many sub-Saharan African cities. Slums are recognized as a menace to planned cities, as they do not conform to planning standards, thus the need to curb their growth. However, this proves to be a challenge for many of these cities due to unavailability of data on the existing situation. It is against this background that this study aims to contribute ground information and a spatial planning tool to support urban planning to better manage slum formation in Lagos, Nigeria. Slum growth can be described as spatial or as population growth; hence this study first analyzed and quantified the spatial growth of slums in Lagos using remote sensing techniques and intensity analysis. Then the influence of residential choices of slum dwellers on population growth in Lagos slums was assessed using ethnographic survey approach through questionnaires and focus group discussions. An urban growth model coupling logistic regression with modified cellular automata SLEUTH was used to simulate scenarios of the patterns of slum development in Lagos by 2035. RapidEye imagery from 2009 and 2015 was used to create maps for each time point for six land-use categories (water, vegetated area, open space, road, slum, and other urban) in the study area. Intensity analysis was applied to quantify the annual intensity of changes at the category and transition level. An overall accuracy (and kappa coefficient) of 94% (0.9) and 89% (0.86) was achieved for the 2009 and 2015 land-use and land-cover maps. The results of this study show that slums in Lagos increased spatially between 2009 and 2015 gaining a land area of 9.14 km2 influenced by in-migration. However, the intensity analysis reveals slum as an active land-use category, losing some of its land area but also gaining new land area during this period. The annual gain and loss was 10.08% and 6.41%, respectively, compared to the uniform intensity of 3.15%. A systematic process of transition was observed between slums and other urban areas and open space in the interval studied, and this process was mainly influenced by the Lagos state government. The transition from slum to other land-use categories, such as other urban, is attributed to gentrification and demolition processes, while the transition from other land-use categories to slum is due to poor maintenance of existing buildings and encroachment on available spaces in the city. Questionnaires administration and focus group discussion were conducted in four communities (Ajegunle, Iwaya, Itire and Ikorodu) in Lagos to investigate the factors influencing the residential choices and reasons of the people to remain in the Lagos slums. Descriptive statistics was used to analyze and describe the factors influencing the residential location choice, and logistic regression was applied to determine the extent to which the neighborhood and household attributes influence slum dwellers’ decisions to remain in the slums. Over 70% of the respondents were migrants from neighboring geopolitical zones (in Nigeria). The movement patterns of slum dwellers in Lagos support two theories of human mobility in slums: slum as a sink and slum as a final destination. Also, the factors that attracted most of the slum dwellers to the slums (cheap housing, proximity to work, etc.) differ from those that made them stay (duration of stay, housing status, etc.). A hybrid land-use model, which involves the coupling of logistic regression with cellular automata SLEUTH, implemented in XULU, was utilized for the simulation of scenarios of slum growth in Lagos. The scenarios were designed based on the modification of the exclusion layer and the transition rules. The scenario 1 was business as usual with slum development similar to the present trend. The scenario 2 was based on the future population projection for the city, while the scenario 3 was based on limited interference by the government in slum development in the city. Distance to markets, shoreline, and local government administrative buildings, and land prices, etc., were predictors of slum development in Lagos. An overall accuracy of 79.17% and a relative operation characteristics (ROC) value of 0.85 were achieved for the prediction of slum development, based on the logistic regression model. The probability map generated from fitting the coefficients of the estimates in the logistic regression shows that slums can develop within the city and at the fringe, and also in places mostly inaccessible to the Lagos state government. Scenarios 1, 2 and 3 predict that the slum area will increase by 1.18 km2, 4.02 km2 and 1.28 km2, respectively, in 2035 through further densification of the existing slums and new development at the south-eastern fringe of the city. The limited growth is due to the high population density in the city, and thus it is assumed that new slums will probably develop in the neighboring cities due to spill over of the Lagos population. The outcome of this research shows that the landscape is very dynamic in Lagos, and even over an interval of a few years, changes can be observed. It also shows that the integration of remote sensing, social science method and spatially explicit land-use model can address the challenges of data availability in the slum dynamic, especially in sub-Saharan African countries with high slum proliferation. This can support a comprehensive set of techniques important for the management of existing slums and prevention of new slum development.Reduzierung des Slumwachstums in der Megastadt Lagos: Ein integrierter Ansatz aus Fernerkundung und urbanem Wachstumsmodell Eine schnelle Urbanisierung bei begrenzter Entwicklung hat in vielen afrikanischen Städten südlich der Sahara zu einer Zunahme von Slums geführt. Slums werden dabei als Bedrohung für die Planstädte angesehen, da sie nicht den Planungsstandards entsprechen, ihr Wachstum sollte daher reduziert werden. Dies erweist sich jedoch für viele dieser Länder als Herausforderung, da keine Daten über die aktuelle Situation vorliegen. Vor diesem Hintergrund zielt diese Studie darauf ab, Informationen und ein Raumplanungsinstrument zur Unterstützung der Stadtplanung zur Verfügung zu stellen, dies soll ein besseres Management der Slumbildung in Lagos, Nigeria ermöglichen. Slumwachstum kann als räumliches Wachstum, oder als Wachstum der Bevölkerung bezeichnet werden; daher hat diese Studie zunächst das räumliche Wachstum von Slums in Lagos mit Hilfe von Fernerkundungstechniken und Intensitätsanalysen analysiert und quantifiziert. Anschließend wurde der Einfluss der Wohnortwahl von Slumbewohnern auf das Bevölkerungswachstum in den Slums von Lagos mit Hilfe eines ethnographischen Erhebung Ansatzes bewertet. Dabei kamen Fragebögen und Fokusgruppendiskussionen zum Einsatz. Ein urbanes Wachstumsmodell, das die logistische Regression mit dem modifizierten zellulären Automaten SLEUTH koppelt, wurde verwendet, um Szenarien und Strukturen der Slumentwicklung in Lagos bis 2035 zu simulieren. RapidEye-Datenaus den Jahren 2009 und 2015 wurden verwendet, um Karten zu jeden Zeitpunkt für sechs Landnutzungskategorien (Wasser, Vegetationsflächen, Freiflächen, Straßen, Slum und andere städtische Gebiete) zu erstellen. Mit Hilfe der Intensitätsanalyse wurde die jährliche Intensität der Veränderungen hinsichtlich der Kategorien und Veränderungstypen quantifiziert. Für die Landnutzungs- und Bodenbedeckungskarten 2009 und 2015 wurde eine Gesamtgenauigkeit (und ein Kappa-Koeffizient) von 94 % (0,9) und 89 % (0,86) erreicht. Die Ergebnisse dieser Studie zeigen, dass die Slums in Lagos zwischen 2009 und 2015 räumlich gewachsen sind und durch Zuzug eine Landfläche von 9,14 km2 erreicht haben. Die Intensitätsanalyse zeigt auch, dass der Slums in Lagos als aktive Landnutzungskategorie einen Teil ihrer Fläche im Beobachtungszeitraum verloren haben. Der jährliche Gewinn und Verlust betrug 10,08 % bzw. 6,41 % im Vergleich zur einheitlichen Intensität von 3,15 %. Ebenfalls wurde ein systematischer Prozess des Übergangs zwischen Slums und anderen städtischen Gebieten sowie Freiraum in der untersuchten Zeitspanne beobachtet. Dieser Prozess wurde hauptsächlich von der Regierung von Lagos beeinflusst. Der Übergang von Slum zu anderen Landnutzungskategorien, wie zum Beispiel andere städtische Gebiete ist auf Gentrifizierung und Abrissprozesse zurückzuführen, während der Übergang von anderen Landnutzungskategorien hin zu Slums auf eine schlechte Instandhaltung bestehender Gebäude und auf die Beeinträchtigung der verfügbaren Flächen in der Stadt zurückzuführen ist. In vier Gemeinden (Ajegunle, Iwaya, Itire and Ikorodu) in Lagos wurden Umfragen mit Fragebögen und Fokusgruppendiskussionen durchgeführt, um die Faktoren zu untersuchen, welche die Wahl des Wohnortes beeinflussen, und um zu untersuchen, warum die Menschen in den Slums von Lagos bleiben. Mit Hilfe der deskriptiven Statistik wurden die Faktoren analysiert und beschrieben, die die Wahl des Wohnortes beeinflussen, und mit Hilfe der logistischen Regression wurde ermittelt, inwieweit die Nachbarschafts- und Haushaltsattribute die Entscheidung der Slumbewohner, in den Slums zu bleiben, beeinflussen. Über 70 % der Befragten waren Migranten aus benachbarten geopolitischen Zonen (Lagos). Die Bewegungsmuster der Slumbewohner in Lagos unterstützen zwei Theorien der menschlichen Mobilität in Slums: Der Slum als Senke oder Endziel. Auch die Faktoren, die die meisten Slumbewohner in die Slums lockten (günstiger Wohnraum, Nähe zum Arbeitsplatz usw.), unterscheiden sich von denen, die sie am Ende zum Bleiben brachten (Aufenthaltsdauer, Wohnstatus usw.). Ein hybrides Landnutzungsmodell, das eine Kopplung der logistischen Regression mit den zellulären Automaten SLEUTH in XULU verbindet, wurde für die Simulation von Szenarien des Slumwachstums in Lagos bis zum Jahr 2035 verwendet. Die Szenarien wurden mittels der Ausschlussflächen und Wachstumskoeffizienten implementiert. Das Szenario 1 „business as usual“ simulierte eine Slumentwicklung ähnlich dem aktuellen Trend. Das Szenario 2 basierte auf der generellen Bevölkerungsprognose für die Stadt, während das Szenario 3 eine begrenzte Einmischung der Regierung auf die Slumentwicklung in der Stadt einbezieht. Die Entfernung zu Märkten, Verwaltungseinrichtungen, Küsten sowie die Grundstückspreisen usw. waren Antriebskräfte für die Entwicklung der Slums in Lagos. Für die Vorhersage der Slum-Entwicklung auf Basis des logistischen Regressionsmodells wurden eine Gesamtgenauigkeit von 79,17 % und einem Receiver-Operating-Characteristic-Wert (ROC) von 0,85 erreicht. Die Wahrscheinlichkeitskarte, die durch die Anpassung der Koeffizienten und der Schätzungen in der logistischen Regression erzeugt wurde, zeigt, dass sich Slums innerhalb der Stadt und in der Peripherie entwickeln können, aber auch an Orten, die dem Einfluss der Landesregierung von Lagos weitgehend entzogen sind. Szenarien 1, 2 und 3 prognostizieren, dass das Slumgebiet bis 2035 durch weitere Verdichtung der bestehenden Slums und Neuentwicklung am südöstlichen Stadtrand um 1,18 km2, 4,02 km2 bzw. 1,28 km2 zunehmen wird. Das relativ begrenzte Wachstum ist auf die hohe Bevölkerungsdichte in der Stadt zurückzuführen, so dass davon ausgegangen wird, dass sich in den Nachbarstädten durch das Verlagern der Lagos-Bevölkerung neue Slums entwickeln werden Das Ergebnis dieser Disertation zeigt, dass die Stadtlandschaft in Lagos sehr dynamisch ist, Veränderungen können selbst über einen Zeitraum von nur wenigen Jahren beobachtet werden. Die Ergebnisse zeigen auch, dass eine Integration von Fernerkundung, sozialwissenschaftlicher Methoden und räumlich explizites Landnutzungsmodells das Problem der geringen Datenverfügbarkeit in dynamischen Slums lösen kann. Dies ist besonders hilfreich in afrikanischen Ländern südlich der Sahara mit hoher Slum,proliferation. Umfassende Techniken des Slum-Managements, insbesondere zur Verhinderung der Entstehung von neuen Slums, können so wirksam unterstützt werden

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment