Modelling the impacts of urban upgrading on population dynamics

Due to the rapid pace of urbanization, cities in the global South are growing with most of this growth occurring in informal settlements. Urban upgrading aims to improve living conditions in such settlements by improving the infrastructure but might lead to unexpected effects such as income segregation. InformalCity, a spatially explicit agent-based model, simulates the implications of urban upgrading in an artificial city. Our simulation experiments show that maintenance of the upgraded infrastructure, the scope of upgrading efforts, and timing (early vs. late investments) affect infrastructure quality, housing development and income segregation. However, we also find that urban upgrading interventions can have contradictory effects; for example, maintenance increases the quality of infrastructure and income segregation. Thus, policy makers need to establish clear targets for upgrading projects, and empirical evaluation studies should consider studying the impacts of urban upgrading on an entire city's development rather than limiting them to informal settlements.     

Modelling of an interactive system with an agent-based architecture using Petri nets, application of the method to the supervision of a transport system

This article presents an original method using high level Petri nets for the specification and design of interactive systems. We suggest an agent oriented architecture based on the classic components of an interactive application (application, dialogue control, interface with the application). Our approach is validated via the specification and design of a human–machine interface used in the supervision of a land-based transport system (bus/tramway).     

Patterns of use of an agent-based model and a system dynamics model: The application of patterns of use and the impacts on learning outcomes

A classification system that was developed for the use of agent-based models was applied to strategies used by school-aged students to interrogate an agent-based model and a system dynamics model. These were compared, and relationships between learning outcomes and the strategies used were also analysed. It was found that the classification system could also be applied to the use of the system dynamics model, with the addition of criterion. This means that a classification system exists for both styles of models. The fact that the strategies could be identified, despite differences in the actual model, and the model type, compared to the original study, means that there are implications for training teachers or systems to also identify the strategies. Initial findings of this study identified links between prior knowledge and the strategy chosen, as well as links with learning outcomes.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.