Andrew Crooks

#Earthquake: Twitter as a Distributed Sensor System

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this article we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this with comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the U.S. Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation model tuned to London data, and an agent-based residen- tial location model, for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions, and the relative arbitrariness of such modelling highlighted.

Mapping for the Masses

The authors describe how we are harnessing the power of web 2.0 technologies to create new approaches to collecting, mapping, and sharing geocoded data. The authors begin with GMapCreator that lets users fashion new maps using Google Maps as a base. The authors then describe MapTube that enables users to archive maps and demonstrate how it can be used in a variety of contexts to share map information, to put existing maps into a form that can be shared, and to create new maps from the bottom-up using a combination of crowdcasting, crowdsourcing, and traditional broadcasting. The authors conclude by arguing that such tools are helping to define a neogeography that is essentially ‘‘mapping for the masses,’’ while noting that there are many issues of quality, accuracy, copyright, and trust that will influence the impact of these tools on map-based communication.

Random planar graphs and the London street network

In this paper we analyse the street network of London both in its primary and dual representation. To understand its properties, we consider three idealised models based on a grid, a static random planar graph and a growing random planar graph. Comparing the models and the street network, we find that the streets of London form a self-organising system whose growth is characterised by a strict interaction between the metrical and informational space. In particular, a principle of least effort appears to create a balance between the physical and the mental effort required to navigate the city.

Map mashups, Web 2.0 and the GIS revolution

Mashups, composed of mixing different types of software and data, first appeared in 2004 and ‘map mashups’ quickly became the most popular forms of this software blending. This heralded a new kind of geography called ‘Neogeography’ in which non-expert users were able to exploit the power of maps without requiring the expertise traditionally associated, in the geographic world, with cartography and geographic information science, and, in computer science, with data structures and graphics programming. First we suggest the need for a typology of map mashups while arguing that such a typology is premature. We then discuss the need for standards and formats, moving on to questions of security, privacy and confidentiality. We follow this by introducing the key issues of creating spatial data for mashups through crowd-sourcing. To ground this presentation in applications, we explore some classic exemplars from our own and related work with map mashups and portals such as MapTube (http://www.maptube.org/). We then point to extensions to other graphical media, to 3D, to virtual worlds and beyond. In conclusion, we speculate on what all this might mean for GIS software and geographic information science.

Introduction to Agent-Based Modelling

The application of agent-based modelling (ABM) to simulating dynamics within geographical systems has seen a considerable increase over the last decade. ABM allows the disaggregation of systems into individual components that can potentially have their own characteristics and rule sets. This is a powerful paradigm that can be exploited through simulation to further our knowledge of the workings of geographical systems. We present in this chapter an overview of ABM; the main features of an agent-based model are given, along with a discussion of what constitutes an agent-based model. The distinction between cellular automata (CA), microsimulation (MSM) and agent-based models are discussed along with the advantages and limitations of ABM for modelling geographical systems. We conclude with a brief discussion of important areas for further research.

Constructing and implementing an agent-based model of residential segregation through vector GIS

In this article, we present a geographically explicit agent-based model (ABM), loosely coupled with vector geographical information systems (GISs), which explicitly captures and uses geometric data and socioeconomic attributes in the simulation process. The ability to represent the urban environment as a series of points, lines, and polygons not only allows one to represent a range of different-sized features such as buildings or larger areas portrayed as the urban environment but is a move away from many ABMs utilizing GIS that are rooted in grid-based structures. We apply this model to the study of residential segregation, specifically creating a Schelling (1971) type of model within a hypothetical cityscape, thus demonstrating how this approach can be used for linking vector-based GIS and agent-based modeling. A selection of simulation experiments are presented, highlighting the inner workings of the model and how aggregate patterns of segregation can emerge from the mild tastes and preferences of individual agents interacting locally over time. Furthermore, the article suggests how this model could be extended and demonstrates the importance of explicit geographical space in the modeling process.

NeoGeography and Web 2.0: concepts, tools and applications

In this article, we explore the concepts and applications of Web 2.0 through the new media of NeoGeography and its impact on how we collect, interact and search for spatial information. We argue that location and space are becoming increasingly important in the information technology revolution. To this end, we present a series of software tools which we have designed to facilitate the non-expert user to develop online visualisations which are essentially map-based. These are based on Google Map Creator, which can produce any number of thematic maps which can be overlaid on Google Maps. We then introduce MapTube, a technology to generate an archive of shared maps, before introducing Google Earth Creator, Image Cutter and PhotoOverlay Creator. All these tools allow users to display and share information over the web. Finally, we present how Second Life has the potential to combine all aspects of Web 2.0, visualisation and NeoGeography in a single multi-user three-dimensional collaborative environment.

The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation

Within this chapter we focus on the integration of Geographical Information System (GIS) and Agent-based modelling (ABM) and review a selection of toolkits which allow for such integration. Moreover, we identify current capabilities of modelling within a GIS and methods of coupling and integrating GIS with agent-based models. We then introduce suggested guidelines for developing geospatial simulations with ABM toolkits and offer practical guidelines for choosing a simulation/modelling system before providing a review of a number of simulation/modelling systems that allow for the creation of geospatial agent based models along with the identification of a number references for further information.

Geosocial gauge: a system prototype for knowledge discovery from social media

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.