Landscape is a heterogeneous collection of visibly distinct features of various elements of land and its various forms on the earth surface. Its pattern is subjected to disturbances and undergo rapid alterations in its grain sizes. The evolving patterns of landscape define and decide various parameters for the planning and management of resources. These dynamic systems possess both spatial and temporal complexity. Exploitation of natural resources and drastic land cover changes have given rise to significant impacts on ecosystem structure and dynamics. The functional abilities (bio-geo chemical cycling, hydrological cycling, etc.) of the landscape are basically dependent on the structure and its complexity. This necessitates inventorying, mapping and modeling of landscape dynamics. Patterns and scale are central issues that are essential to understand complex interactions and driving forces. Large scale changes have been rapid and occurring since industrialization and urbanisation in the last century. The exponential growth of cities has been noticed since the industrial revolution and as transport sector changed the mobility of the masses drastically. Urbanisation interacts with the neighboring landscape structures in the form of commuter’s flow, pollution, obtaining food grain, which create dispersed growth or sprawl in between the metropolis and the semi urban area, and these areas are often devoid of basic amenities due to lack of prior information and necessitates predictions of such growth while planning, policy and decision-making. Planning determines appropriate future action through a sequence of choices that tend to occur. To understand uncertain conditions, planners and city managers need vital comprehensive information about the temporally evolving landscape and try to predict the future, for effective decisions. The quality of planning and its decision processes can be substantially improved when the required information is handled appropriately and efficiently. This explains that an effective planning requires descriptive, predictive, and prescriptive information inputs for sustainable resource management. Therefore, modeling future trends becomes a necessary part of planning. Urban growth models help in modelling future trends that can be an efficient and effective support tool. In recent years, the confluence of developments in Remote sensing, Geographic Information System and Image processing, Computational Urban Growth and Urban Land-use Modeling has made possible in timely provision of information inputs to planners. In the context of Indian cities, this research attempts to study the patterns of urban growth and the rate of change of that growth using various techniques such as Land use, land cover models, Gradient and zonal approach, spatial metrics and urban growth models. Indian cities are divided based on population into various categories. These categories were considered separately and dealt with sample number of cities. This works helps in understanding the change pattern of rapidly urbanising, moderately urbanising and rural landscape is accomplished using various metrics and gradients. The research, is mainly aimed at understanding the pattern of growth and device computational urban growth model using well known techniques and develop a suitable technique in order to understand the context of agents and their role in modelling future urban growth and estimate the rate of loss of other land use categories due to urban growth. Satellite images for different time series was used to study the pattern of urban growth in the study areas. Well know indicators were derived from the data. This was further used to model one of the rapidly urbanising cities based on scenario no agents/factor and with agents of growth using city development plans and in absence of it. This adaptation to Indian context will help in gaining better understanding of the urban growth system in various levels of cities classified, and thus help in providing inputs and specific information of future growth for urban planners and city managers to provide better basic amenities and for sustainable growth of cities. The objective of the proposed research is to understand and model the spatio temporal patterns of landscape dynamics. This involves i. Analysis of Landscape dynamics using multi-resolution (spatial, temporal and spectral) data. ii. Quantifying landscape dynamics using landscape metrics and associated landscape parameters. iii. Modeling and geo-visualisation of landscape dynamics in rapidly urbanizing, moderately urbanising and rural landscape using these parameters. iv. Model the landscape dynamics using soft computing techniques. The thesis consists of nine chapters. Chapter 1 introduces the basic concepts such as landscape, landscape dynamics, use of spatio-temporal data to monitor landscape dynamics, geo-visualisation of landscape dynamics, research gaps and motivation for taking up the research in this domain. Chapter 2 presents the study region, which are broadly grouped as (i) Rapidly urbanizing landscapes (corresponding to Tier I Cities in India), (ii) Moderately urbanizing landscapes (Tier II cities, chosen select Tier II cities in Karnataka), and (iii) Landscape experiencing minimal urbanisation (rural landscape). Chapter 3 discusses the material and method adopted for understanding landscape dynamics and geo-visualisation of landscape dynamics Chapter 4 presents the landscape dynamics in rapidly urbanizing landscape (Bangalore) in India. Spatial pattern analyses are done through metrics using zonal- gradient approach. Chapter 5 analyses the environmental sustainability aspects considering one case study of rapidly urbanizing landscape – Bangalore Chapter 6 discusses urbanisation process and patterns across macro cities in India. Similarly Chapter 7 discusses the urbanisation pattern in Tier II cities (in Karnataka) and Chapter 8 presents the rural landscape dynamics Geo-visualisation of a rapidly urbanizing landscape (Bangalore) through techniques such as Cellular Automata – Markov Chain, land change modeler (LCM), Geographical land use change modeler (GEOMOD), Markov Cellular automata based process of deriving agent’s behavior using Fuzziness in the dataset and Analytical Hierarchal process. Further research in progress in this domain focusses on integration of various agents and evaluation of proposed development plans and likely scenario of integrating land use with mobility. Keyword: landscape, landscape dynamics, urbanisation, urban growth, urban sprawl, urban footprint, modelling, geo-visualisation

[ES] La presente tesis doctoral propone la arquitectura de un sistema de Geo-visualización Espaciotemporal de actividad delictiva y criminal, para ser aplicada a Sistemas de Comando y Control (C2S) específicamente dentro de sus Sistemas de Información de Comando y Control (C2IS). El sistema de Geo-visualización Espaciotemporal se basa en el análisis masivo de datos reales de actividad delictiva, proporcionado por la Policía Nacional Colombiana (PONAL) y está compuesto por dos aplicaciones diferentes: la primera permite al usuario geo-visualizar espaciotemporalmente de forma dinámica, las concentraciones, tendencias y patrones de movilidad de esta actividad dentro de la extensión de área geográfica y el rango de fechas y horas que se precise, lo cual permite al usuario realizar análisis e interpretaciones y tomar decisiones estratégicas de acción más acertadas; la segunda aplicación permite al usuario geo-visualizar espaciotemporalmente las predicciones de la actividad delictiva en periodos continuos y cortos a modo de tiempo real, esto también dentro de la extensión de área geográfica y el rango de fechas y horas de elección del usuario. Para estas predicciones se usaron técnicas clásicas y técnicas de Machine Learning (incluido el Deep Learning), adecuadas para el pronóstico en multiparalelo de varios pasos de series temporales multivariantes con datos escasos. Las dos aplicaciones del sistema, cuyo desarrollo se muestra en esta tesis, están realizadas con métodos novedosos que permitieron lograr estos objetivos de efectividad a la hora de detectar el volumen y los patrones y tendencias en el desplazamiento de dicha actividad, mejorando así la conciencia situacional, la proyección futura y la agilidad y eficiencia en los procesos de toma de decisiones, particularmente en la gestión de los recursos destinados a la disuasión, prevención y control del delito, lo cual contribuye a los objetivos de ciudad segura y por consiguiente de ciudad inteligente, dentro de arquitecturas de Sistemas de Comando y Control (C2S) como en el caso de los Centros de Comando y Control de Seguridad Ciudadana de la PONAL. / [CA] Aquesta tesi doctoral proposa l'arquitectura d'un sistema de Geo-visualització Espaitemporal d'activitat delictiva i criminal, per ser aplicada a Sistemes de Comandament i Control (C2S) específicament dins dels seus Sistemes d'informació de Comandament i Control (C2IS). El sistema de Geo-visualització Espaitemporal es basa en l'anàlisi massiva de dades reals d'activitat delictiva, proporcionada per la Policia Nacional Colombiana (PONAL) i està composta per dues aplicacions diferents: la primera permet a l'usuari geo-visualitzar espaitemporalment de forma dinàmica, les concentracions, les tendències i els patrons de mobilitat d'aquesta activitat dins de l'extensió d'àrea geogràfica i el rang de dates i hores que calgui, la qual cosa permet a l'usuari fer anàlisis i interpretacions i prendre decisions estratègiques d'acció més encertades; la segona aplicació permet a l'usuari geovisualitzar espaciotemporalment les prediccions de l'activitat delictiva en períodes continus i curts a mode de temps real, això també dins l'extensió d'àrea geogràfica i el rang de dates i hores d'elecció de l'usuari. Per a aquestes prediccions es van usar tècniques clàssiques i tècniques de Machine Learning (inclòs el Deep Learning), adequades per al pronòstic en multiparal·lel de diversos passos de sèries temporals multivariants amb dades escasses. Les dues aplicacions del sistema, el desenvolupament de les quals es mostra en aquesta tesi, estan realitzades amb mètodes nous que van permetre assolir aquests objectius d'efectivitat a l'hora de detectar el volum i els patrons i les tendències en el desplaçament d'aquesta activitat, millorant així la consciència situacional , la projecció futura i l'agilitat i eficiència en els processos de presa de decisions, particularment en la gestió dels recursos destinats a la dissuasió, prevenció i control del delicte, la qual cosa contribueix als objectius de ciutat segura i per tant de ciutat intel·ligent , dins arquitectures de Sistemes de Comandament i Control (C2S) com en el cas dels Centres de Comandament i Control de Seguretat Ciutadana de la PONAL. / [EN] This doctoral thesis proposes the architecture of a Spatiotemporal Geo-visualization system of criminal activity, to be applied to Command and Control Systems (C2S) specifically within their Command and Control Information Systems (C2IS). The Spatiotemporal Geo-visualization system is based on the massive analysis of real data of criminal activity, provided by the Colombian National Police (PONAL) and is made up of two different applications: the first allows the user to dynamically geo-visualize spatiotemporally, the concentrations, trends and patterns of mobility of this activity within the extension of the geographic area and the range of dates and times that are required, which allows the user to carry out analyses and interpretations and make more accurate strategic action decisions; the second application allows the user to spatially visualize the predictions of criminal activity in continuous and short periods like in real time, this also within the extension of the geographic area and the range of dates and times of the user's choice. For these predictions, classical techniques and Machine Learning techniques (including Deep Learning) were used, suitable for multistep multiparallel forecasting of multivariate time series with sparse data. The two applications of the system, whose development is shown in this thesis, are carried out with innovative methods that allowed achieving these effectiveness objectives when detecting the volume and patterns and trends in the movement of said activity, thus improving situational awareness, the future projection and the agility and efficiency in the decision-making processes, particularly in the management of the resources destined to the dissuasion, prevention and control of crime, which contributes to the objectives of a safe city and therefore of a smart city, within architectures of Command and Control Systems (C2S) as in the case of the Citizen Security Command and Control Centers of the PONAL. / Salcedo González, ML. (2023). Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192685