Jukka M. Krisp

A spatio-temporal population model to support risk assessment and damage analysis for decision-making

The aim of this research is to develop and implement a simple spatio‐temporal model of population location that might improve risk assessment and damage analysis for decision‐making in both the Finnish Fire and Rescue Services and the Finnish Defence Forces. The motivation for the research is that present risk models do not take into account the temporal variation in population location during different times of the day. We use spatio‐temporal modelling methods to model the population dynamics, and visualization techniques to represent the model outcomes. In addition, we apply the developed model to a damage‐analysis application. The case study site is located in the centre of Helsinki. The model uses a basic population and workplace dataset maintained by the Helsinki Metropolitan Area Council. By means of this model, we intend to advance risk assessment, which considers the consequences of accidents. This model has the potential to help decision‐makers evaluate their plans in several application areas—such as achieving better preparedness by having more reliable evacuation plans and resource allocation. In addition to the application‐related technological research, a more generic framework about decision‐making supported by spatio‐temporal knowledge and visualization is presented.

Progress in Location-Based Services

The book consists of peer-reviewed papers from the 9th symposium on Location Based Services (LBS) which is targeted to researchers, industry/market operators and students of different backgrounds (scientific, engineering and humanistic). As the research field is developing and changing fast, this book follows up on current trends and gives suggestions and guidance to further research. This book offers a common ground bringing together various disciplines and practice, knowledge, experiences, plans and ideas on how LBS can and could be improved and on how it will influence both science and society. The book comprises front-end publications organized into sections on: spatial-temporal data acquisition, processing & analysis; positioning / indoor positioning; way-finding / navigation (indoor / outdoor) & smart mobile phone navigation; interactions, user studies and evaluations; innovative LBS systems & applications.

Georeferencing: a review of methods and applications

Various applications, ranging from map creation tools to navigation systems, employ methods introduced by the domain of georeferencing, which investigates techniques for uniquely identifying geographical objects. This paper aims to provide an overview of ongoing challenges of the georeferencing domain by presenting, classifying and exploring the field and its relevant methods and applications.

Car navigation – computing routes that avoid complicated crossings

Personalized navigation and way-finding are prominent research areas of location-based service (LBSs). This includes innovative concepts for car navigation. Within this paper, we investigate the idea of providing drivers a routing suggestion which avoids ‘complicated crossings’ in urban areas. Inexperienced drivers include persons who have a driver’s license but, for whatever reason, feel uncomfortable to drive in a city environment. Situations where the inexperienced driver has to depend on a navigation device and reach a destination in an unfamiliar territory may be difficult. Preferences of inexperienced drivers are investigated. ‘Fears’ include driving into ‘complicated crossings’. Therefore, the definition and spatial characteristics of ‘complicated crossings’ are investigated. We use OpenStreetMap as a road dataset for the routing network. Based on the topological characteristics of the dataset, measured by the number of nodes, we identify crossings that are ‘complicated’. The user can choose to compute an alternative route that avoids these complicated crossings. This methodology is one step in building a full ‘inexperienced drivers’ routing system, which includes additional preferences from the user group, for example, as avoiding left turns where no traffic light is present.

Planning Fire and Rescue Services by Visualizing Mobile Phone Density

This paper focuses on how calculating and visualizing mobile phone density would assist fire and rescue services in Helsinki, Finland. Studying the relationship between population distributions (over time) and population density hot spots can lead to better emergency preparedness and the more efficient allocation of fire and rescue services. Current data are restricted to the administrative boundaries of census data and limited in their time dimension. Data acquired from mobile phone locations have a high temporal and spatial dimension and are better suited for the purpose of showing changing population density hotspots over time. Viewing population density as a continuous surface, using kernel density estimations, and visualizing it as a “landscape” can support the understanding of density distributions, which are of particular importance in the planning of fire and rescue services.

Directed kernel density estimation (DKDE) for time series visualization

The purpose of this article is to investigate the density calculation and representation of spatially and temporally highly dynamic point data sets. We suggest an approach to explore point patterns that have a temporal dimension and therefore introduce an incremental development of the traditional kernel density estimation processes. Based on a movement vector assigned to each moving point, we apply a directed (or tilted) kernel to calculate and visualize the density pattern. The resulting density map recognizes the dynamic behavior of the underlying data points. By applying a shade effect or contour lines, the areas with densely distributed moving points are characterized by directed ‘ripples’ or ‘waves’. This assists the visual analysis and prediction of ‘movement trends’ based on the dynamic points. In our case study, we apply this method to airplane movement data limited to two points in time and we thereby visualize the results for the study area over Germany.

Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation

“Classic” kernel density estimations (KDE) can display static densities representing one point in time. It is not possible to visually identify which parts of the densities are moving. Therefore, within this paper we investigate how to display dynamic densities (and the density changes) to identify movement patterns. To deal with a temporal dimension (in our case study a dynamic crowd of individuals) we investigated the application of directed kernel density estimation (DKDE). In a case study we apply the DKDE to a point dataset presenting individuals approaching the Allianz Arena in Munich, Germany, with different speeds from different directions. Calculating the density using a directed kernel with this data, results in a density map indicating the movement direction with a visible “ripple” effect. Ripples move at different rates to the substances in which they occur. That tells us something about crowd dynamics and enables us to visually recognize the parts of the crowds that are moving plus the underlying movement directions.

How Many 3D City Models Are There? – A Typological Try

Abstract Three-dimensional (3D) city models are intended to represent the geometry along with the appearance of the reality. In some cases, photos are precisely mapped as texture onto the geometric model, which then renders a realistic impression of the underlying environment. In other cases, the models are visualized in a more abstract way. This paper is dedicated to investigating the relation of level of abstraction, information density and storage capacity for the representation of a 3D city model. Increasing the level of abstraction usually leads to the decrease of information density to be processed. Accordingly, the general storage capacity will decrease as well. Since a higher level of abstraction is less bound to photorealistic aspects, it may allow further semantic information to be accommodated in the visualisation process. If the level of abstraction in a city model increases in a well-controlled way so that the characteristic features are enhanced while non-relevant information is inhibited, the user’s cognitive load during task completion may be reduced. This implies an increased efficiency of information communication. Using image segmentation algorithms, the elements within a facade per unit of area are successively reduced while preserving the characteristic structures within the image. The segmented image holds the sufficient information to be recognized in the environment. We treat the three concepts, level of abstraction, information density and storage capacity, as fundamental variables for the visualisation of building facades. To deal with these concepts, we organizing them on a continuum, which enables us to find a tradeoff between a potential loss of information and the level of usage for various tasks. Les modèles urbains 3D sont supposés représenter la géométrie avec une apparence de réalité. Dans certains cas, des photographies sont plaquées sur le modèle géométrique ce qui donne de fait une impression réaliste à l’environnement. Dans d’autres cas, la représentation est plus abstraite. Ce papier décrit les relations entre le niveau de détail, la densité d’information et les capacités de stockage des modèles 3D urbains. L’augmentation du niveau d’abstraction va généralement de pair avec la baisse de densité d’information à traiter et à stocker. Dans la mesure où un niveau d’abstraction supérieur est moins lié à une représentation photo-réaliste, cela permet de représenter davantage d’informations sémantiques lors de la visualisation. Si le niveau d’abstraction du modèle urbain croît de façon contrôlée afin que les objets caractéristiques soient mis en valeur et les objets secondaires soient désactivés, alors la charge cognitive de l’utilisateur pendant le processus de complètement sera moindre. Cela produit une plus grande efficacité de l’information communiquée. En utilisant des algorithmes de segmentation d’images, les éléments à l’intérieur d’une façade par unité de surface sont progressivement réduits tout en préservant les caractéristiques structurelles à l’intérieur de l’image. L’image segmentée contient l’information suffisante pour être reconnue dans son environnement. Nous considérons donc trois concepts – niveau de détail, densité d’information et capacité de stockage – comme étant les variables fondamentales pour la visualisation des façades des bâtiments. Pour prendre en compte ces concepts, nous les organisons selon un continuum ce qui nous permet de trouver des équilibres entre la perte possible d’information et le niveau d’utilisation pour différentes tâches.

Kernel Density Estimations for Visual Analysis of Emergency Response Data

The purpose of this chapter is to investigate the calculation and representation of geocoded fire & rescue service missions. The study of relationships between the incident distribution and the identification of high (or low) incident density areas supports the general emergency preparedness planning and resource allocation. Point density information can be included into broad risk analysis procedures, which consider the spatial distribution of the phenomena and its relevance to other geographical and socio-economical data (e.g., age distribution, workspace distribution). The service mission reports include individual points representing the x/y coordinates of the incident locations. These points can be represented as a continuous function to result in an effective and accurate impression of the incident distribution. The continuity is recognized by kernel density calculations, which replaces each point with a three-dimensional moving function. This method allows to control the degree of density smoothing by the search radius (also referred to as bandwidth) of the kernels. The choice of the kernel bandwidth strongly influences the resulting density surface. If the bandwidth is too large the estimated densities will be similar everywhere and close to the average point density of the entire study area. When the bandwidth is too small, the surface pattern will be focused on the individual point records. Experimentation is necessary to derive the optimal bandwidth setting to acquire a satisfactory case-specific density surface. The kernel density tools provided in standard GIS (like ArcGIS) software suggest a default calculation of the search radius based on the linear units based on the projection of the output spatial reference, which seems to be inadequate to display incident density. Within this chapter we provide a flexible approach to explore the point patterns by displaying the changes in density representation with changing search radius. We will investigate how the parameters can be optimized for displaying incident distribution in relation to the service areas of Fire and Rescue stations.

Exploring geographical data with spatio-visual data mining

Efficiently exploring a large spatial dataset with the aim of forming a hypothesis is one of the main challenges for information science. This study presents a method for exploring spatial data with a combination of spatial and visual data mining. Spatial relationships are modeled during a data pre-processing step, consisting of the density analysis and vertical view approach, after which an exploration with visual data mining follows. The method has been tried on emergency response data about fire and rescue incidents in Helsinki.