Sensor-Driven, Spatially Explicit Agent-Based Models

Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting

Mapping Rural Road Networks from Global Positioning System (GPS) Trajectories of Motorcycle Taxis in Sigomre Area, Siaya County, Kenya

Effective transport infrastructure is an essential component of economic integration, accessibility to vital social services and a means of mitigation in times of emergency. Rural areas in Africa are largely characterized by poor transport infrastructure. This poor state of rural road networks contributes to the vulnerability of communities in developing countries by hampering access to vital social services and opportunities. In addition, maps of road networks are incomplete, and not up-to-date. Lack of accurate maps of village-level road networks hinders determination of access to social services and timely response to emergencies in remote locations. In some countries in sub-Saharan Africa, communities in rural areas and some in urban areas have devised an alternative mode of public transport system that is reliant on motorcycle taxis. This new mode of transport has improved local mobility and has created a vibrant economy that depends on the motorcycle taxi business. The taxi system also offers an opportunity for understanding local-level mobility and the characterization of the underlying transport infrastructure. By capturing the spatial and temporal characteristics of the taxis, we could design detailed maps of rural infrastructure and reveal the human mobility patterns that are associated with the motorcycle taxi system. In this study, we tracked motorcycle taxis in a rural area in Kenya by tagging volunteer riders with Global Positioning System (GPS) data loggers. A semi-automatic method was applied on the resulting trajectories to map rural-level road networks. The results showed that GPS trajectories from motorcycle taxis could potentially improve the maps of rural roads and augment other mapping initiatives like OpenStreetMap(VLID)286170

An adaptive agent-based model of homing pigeons : a genetic algorithm approach

Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In todays data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.(VLID)219568

A Pragmatic Analysis of Margaret Ogola’s the River and the Source and I Swear by Apollo

This paper aims at interrogating the significance of pragmatics in analyzing Margaret Ogola’s two novels; The River and the Source (1994) and I swear by Apollo (2002). The researcher analyses the characters’ conversational turns in the novels using Austin (1962) and Searle 1969 Speech Act Theory. The study employs an analytical research design using a mixed method data analysis. The findings indicate that every utterance used by a character performs three simultaneous acts namely; a locutionary, an illocutionary and perlocutionary. In addition, the data shows that every utterance produced by a character in the novels could be categorized under one of the five major categories speech acts proposed by Searle (1969); representatives, expressive, directives, commisives or declarations. The study found that the representatives are the most dominant in both novels while declarations the least. The study also reveals that each major speech act contains a wide range of sub acts or illocutionary forces which are distinguished based on their felicity conditions. The study therefore proposes that pragmatic analysis be adopted as an effective tool in the analysis of the characters’ verbal interactions in novels. In addition, further research could be conducted on pragmatic analysis of novels by other writers

Spatial Modelling of Solar energy Potential in Kenya

Solar energy is one of the readily available renewable energy resources in the developing countries within the tropical region. Kenya is one of the countries which receive an average of approximately 6.5 sunshine hours in a single day throughout the year. However, there is slow adoption of solar energy resources in the country due to limited information on the spatial variability solar energy potential. This study aims at assessing the potential of photovoltaic solar energy in Kenya. The factors that influence incident solar radiation which were considered in this task included atmospheric transmissivity and topography. The influence of atmospheric transmissivity was factored in by modelling monthly transmissivity factors from a combination of cloud cover, diffuse ratios and the effect of altitude. The contribution of topography was included by applying hemispherical viewshed analysis to determine the amount of incident global radiation on the surface based on the orientation of the terrain. GIS concepts were used to integrate the spatial datasets from different themes. The results showed that, about 70% of the land area in Kenya has the potential of receiving approximately 5kWh/m2/day throughout the year. In outline, this work successfully assessed the spatio-temporal variability in the characteristics of solar energy potential in Kenya and can be used as a basis for policy support in the country

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In today’s data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis

A model-sensor framework to predict homing pigeon flights in real time . GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Advances in sensor technology have triggered the development of model-sensor frameworks that integrate real-time sensor data into simulation models. Successful implementations include sensor streams of physical properties of the environment, and traces of human mobility as captured by mobile devices. Implementation of real-time sensor-data streams from tagged animals is not yet in place. However, these are anticipated in the near future. In this research, we explore a model-sensor framework with a novel agent-based flocking model for homing pigeons and an emulated sensor stream that is derived from a previously recorded GPS track. Results point to conceptual shortcomings but also opportunities in transferring conventional models to an integrated model-sensor framework. From these insights, directions for further research towards automatic model-calibration and adaptive rulesets were identified

A model-sensor framework to predict homing pigeon flights in real time . GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Advances in sensor technology have triggered the development of model-sensor frameworks that integrate real-time sensor data into simulation models. Successful implementations include sensor streams of physical properties of the environment, and traces of human mobility as captured by mobile devices. Implementation of real-time sensor-data streams from tagged animals is not yet in place. However, these are anticipated in the near future. In this research, we explore a model-sensor framework with a novel agent-based flocking model for homing pigeons and an emulated sensor stream that is derived from a previously recorded GPS track. Results point to conceptual shortcomings but also opportunities in transferring conventional models to an integrated model-sensor framework. From these insights, directions for further research towards automatic model-calibration and adaptive rulesets were identified

Sustainability / Predicting Migratory Corridors of White Storks, Ciconia ciconia, to Enhance Sustainable Wind Energy Planning : A Data-Driven Agent-Based Model

White storks (Ciconia ciconia) are birds that make annual long-distance migration flights from their breeding grounds in the Northern Hemisphere to the south of Africa. These trips take place in the winter season, when the temperatures in the North fall and food supply drops. White storks, because of their large size, depend on the wind, thermals, and orographic characteristics of the environment in order to minimize their energy expenditure during flight. In particular, the birds adopt a soaring behavior in landscapes where the thermal uplift and orographic updrafts are conducive. By attaining suitable soaring heights, the birds then use the wind characteristics to glide for hundreds of kilometers. It is therefore expected that white storks would prefer landscapes that are characterized by suitable wind and thermal characteristics, which promote the soaring and gliding behaviors. However, these same landscapes are also potential sites for large-scale wind energy generation. In this study, we used the observed data of the white stork movement trajectories to specify a data-driven agent-based model, which simulates flight behavior of the white storks in a dynamic environment. The data on the wind characteristics and thermal uplift are dynamically changed on a daily basis so as to mimic the scenarios that the observed birds experienced during flight. The flight corridors that emerge from the simulated flights are then combined with the predicted surface on the wind energy potential, in order to highlight the potential risk of collision between the migratory white storks and hypothetical wind farms in the locations that are suitable for wind energy developments. This work provides methods that can be adopted to assess the overlap between wind energy potential and migratory corridors of the migration of birds. This can contribute to achieving sustainable trade-offs between wind energy development and conservation of wildlife and, hence, handling the issues of humanwildlife conflicts.(VLID)262471

Assessing the Link between Human Modification and Changes in Land Surface Temperature in Hainan, China Using Image Archives from Google Earth Engine

In many areas of the world, population growth and land development have increased demand for land and other natural resources. Coastal areas are particularly susceptible since they are conducive for marine transportation, energy production, aquaculture, marine tourism and other activities. Anthropogenic activities in the coastal areas have triggered unprecedented land use change, depletion of coastal wetlands, loss of biodiversity, and degradation of other vital ecosystem services. The changes can be particularly drastic for small coastal islands with rich biodiversity. In this study, the influence of human modification on land surface temperature (LST) for the coastal island Hainan in Southern China was investigated. We hypothesize that for this island, footprints of human activities are linked to the variation of land surface temperature, which could indicate environmental degradation. To test this hypothesis, we estimated LST changes between 2000 and 2016 and computed the spatio-temporal correlation between LST and human modification. Specifically, we classified temperature data for the four years 2000, 2006, 2012 and 2016 into 5 temperature zones based on their respective mean and standard deviation values. We then assessed the correlation between each temperature zone and a human modification index computed for the year 2016. Apart from this, we estimated mean, maximum and the standard deviation of annual temperature for each pixel in the 17 years to assess the links with human modification. The results showed that: (1) The mean LST temperature in Hainan Island increased with fluctuations from 2000 to 2016. (2) The moderate temperature zones were dominant in the island during the four years included in this study. (3) A strong positive correlation of 0.72 between human modification index and mean and maximum LST temperature indicated a potential link between human modification and mean and maximum LST temperatures over the 17 years of analysis. (4) The mean value of human modification index in the temperature zones in 2016 showed a progressive rise with 0.24 in the low temperature zone, 0.33 in the secondary moderate, 0.45 in the moderate, 0.54 in the secondary high and 0.61 in the high temperature zones. This work highlighted the potential value of using large and multi-temporal earth observation datasets from cloud platforms to assess the influence of human activities in sensitive ecosystems. The results could contribute to the development of sustainable management and coastal ecosystems conservation plans