Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models

Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects

Spatiotemporal Changes of Farming-Pastoral Ecotone in Northern China, 1954–2005: A Case Study in Zhenlai County, Jilin Province

Analyzing spatiotemporal changes in land use and land cover could provide basic information for appropriate decision-making and thereby plays an essential role in promoting the sustainable use of land resources, especially in ecologically fragile regions. In this paper, a case study was taken in Zhenlai County, which is a part of the farming-pastoral ecotone of Northern China. This study integrated methods of bitemporal change detection and temporal trajectory analysis to trace the paths of land cover change for every location in the study area from 1954 to 2005, using published land cover data based on topographic and environmental background maps and also remotely sensed images including Landsat MSS (Multispectral Scanner) and TM (Thematic Mapper). Meanwhile, the Lorenz curve and Gini coefficient derived from economic models were also used to study the land use structure changes to gain a better understanding of human impact on this fragile ecosystem. Results of bitemporal change detection showed that the most common land cover transition in the study area was an expansion of arable land at the expense of grassland and wetland. Plenty of grassland was converted to other unused land, indicating serious environmental degradation in Zhenlai County during the past decades. Trajectory analysis of land use and land cover change demonstrated that settlement, arable land, and water bodies were relatively stable in terms of coverage and spatial distribution, while grassland, wetland, and forest land had weak stability. Natural forces were still dominating the environmental processes of the study area, while human-induced changes also played an important role in environmental change. In addition, different types of land use displayed different concentration trends and had large changes during the study period. Arable land was the most decentralized, whereas forest land was the most concentrated. The above results not only revealed notable spatiotemporal features of land use and land cover change in the time series, but also confirmed the applicability and effectiveness of the methodology in our research, which combined bitemporal change detection, temporal trajectory analysis, and a Lorenz curve/Gini coefficient in analyzing spatiotemporal changes in land use and land cover

AN INTEGRATED APPROACH FOR SIMULATION AND PREDICTION OF LAND USE AND LAND COVER CHANGES AND URBAN GROWTH (CASE STUDY: SANANDAJ CITY IN IRAN)

One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regression-cellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas

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

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

Geospatial approach using socio-economic and projected climate change information formodelling urban growth

Urban growth and climate change are two interwoven phenomena that are becoming global environmental issues. Using Niger Delta of Nigeria as a case study, this research investigated the historical and future patterns of urban growth using geospatialbased modelling approach. Specific objectives were to: (i) examine the climate change pattern and predict its impact on urban growth modelling; (ii) investigate the historical pattern of urban growth; (iii) embrace some selected parameters from United Nations Sustainable Development Goals (UN SDGs) and examine their impacts on future urban growth prediction; (iv) verify whether planning has controlled urban land use sprawl in the study area; and (v) propose standard operating procedure for urban sprawl in the area. A MAGICC model, developed by the Inter-Governmental Panel on Climate Change (IPCC), was used to predict future precipitation under RCP 4.5 and RCP 8.5 emission scenarios, which was utilized to evaluate the impact of climate change on the study area from 2016 to 2100. Observed precipitation records from 1972 to 2015 were analysed, and 2012 was selected as a water year, based on depth and frequency of rainfall. A relationship model derived using logistic regression from the observed precipitation and river width from Landsat imageries of 2012 was used to project the monthly river width variations over the projected climate change, considering the two emission scenarios. The areas that are prone to flooding were determined based on the projected precipitation anomalies and a suitability map was developed to accommodate the impact of climate change in the projection of future urban growth. Urban landscape changes between 1985 and 2015 were also analysed, which revealed a rapid urban growth in the region. A Cellular Automata/Markov Chain (CA-Markov) model was used to project the year 2030 land cover of the region considering two scenarios; normal projection without any constraint, and using some designed constraints (forest reserves, population and economy) based on some selected UN SDGs criteria and climate change. On validation, overall simulation accuracies of 89.25% and 91.22% were achieved based on scenarios one and two, respectively. The projection using the first scenario resulted to net loss and gains of - 7.37%, 11.84% and 50.88%, while that of second scenario produced net loss and gains of -4.72%, 7.43% and 48.37% in forest, farmland and built-up area between 2015 and 2030, respectively. The difference between the two scenarios showed that the UN SDGs have great influence on the urban growth prediction and strict adherence to the selected UN SDGs criteria can reduce tropical deforestation, and at the same time serve as resilience to climate change in the region

Multi-layer perceptron - markov chain based geospatial analysis of land use and land cover change: A case study of Stoney Creek Watershed, BC, Canada

This thesis study analyzed the land use and land cover (LULC) changes in Stoney Creek Watershed, BC, Canada using the combination of remote sensing, GIS and modeling approaches. The Object-Based Image Analysis (OBIA) tool in PCI Geomatica 2017 software was applied to generate unsupervised classification LULC maps using Landsat TM and OLI images of the years 1986, 1999 and 2016. Various band ratio were computed to improve different classification results. Esri ArcMap 10.5 was used to produce all the LULC maps for subsequent modeling. A modeling method using Multi-layer perceptron (MLP) neural network and Markov Chain (MC) was performed to predict LULC changes in 2026, using hard and soft prediction results. The outcomes of this study could provide valuable information of LULC patterns and dynamics for supporting both environmental and economic development in this area.land use and land coverStoney Creek WatershedGISobject based image analysisgeomaticaperceptro

Assessing Anthropogenic Dynamics in Megacities from the Characterization of Land Use/Land Cover Changes: The Bogotá Study Case

[EN] Usually, megacities expand without proper planning in a context of demographic growth and are increasingly dependent on the natural resources related to the occupied area. This is a major challenge for the sustainable management of these territories, justifying the need for a better knowledge of land use/land cover (LULC) distribution and characteristics to observe spatial anthropogenic dynamics. In this study, the Bogota river basin and the Bogota megacity were analyzed as a case study. The main objective of this work was to analyze the historical LULC dynamics from 1985 to 2014. Reliable forecasting scenarios were developed using the Land Change Modeler to support sustainable management and planning. Results show an expansion of the Bogota megacity toward the Northeast and an increase of urban areas within the basin. These changes implied a loss of 58% of forest surface, a strategic ecosystem, from 1985 to 2014. 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