Harold J. Ship

Understanding customer behavior using indoor location analysis and visualization

Understanding customer behavior in brick-and-mortar stores and other physical indoor venues is essential for any business aiming to provide a more personal and compelling shopping experience, optimize store layout, and improve store operations. Achieving these goals ultimately leads to improved user experience, conversion rates, and increased revenue. Todays mobile-based location technologies provide information about the users location that can be used in advanced analytics and visualizations. This means retailers and enterprises can gain insight into customer behavior patterns and understand, for example, how much time customers spend in different areas of the store, what routes they take, how well they are serviced, and more. In this paper, we present a solution approach for better understanding customer behavior based on mobile indoor location data as well as the technologies developed by IBM Research for realizing this solution. We describe significant challenges considering collection, curation, analysis, and visualization of indoor location-based data and illustrate the use of the approach for smarter commerce in a real-world use case.

Location and Context-Based Microservices for Mobile and Internet of Things Workloads

Research institutes such as Gartner and Forrester claim that the future of mobile will focus around the users context. Most of the future mobile applications will leverage user context to provide a richer user experience and deeper engagement, and consequently higher customer value. We present three cloud micro services that can substantially accelerate the development and evolvement of location and context-based applications. These include a contextual triggering micro service used to derive the users context in the moment of interaction, and visualization and analytics micro services to distill business and operational insights from application data. These micro services are described alongside mobile and Internet of Things usage examples.

Scalable Detection of Spatiotemporal Encounters in Historical Movement Data

The widespread adoption of location‐aware devices is resulting in the generation of large amounts of spatiotemporal movement data, collected and stored in digital repositories. This forms a fertile ground for domain experts and scientists to analyze such historical data and discover interesting movement behavioral patterns. Experts in many domains, such as transportation, logistics and retail, are interested in detecting and understanding movement patterns and behavior of objects in relation to each other. Their insights can point to optimization potential and reveal deviations from planned behavior. In this paper, we focus on the detection of the encounter patterns as one possible type in movement behavior. These patterns refer to objects being close to one another in terms of space and time. We define scalability as a core requirement when dealing with historical movement data, in order to allow the domain expert to set parameters of the encounter detection algorithm. Our approach leverages a designated data structure and requires only a single pass over chronological data, thus resulting in highly scalable and fast technique to detect encounters. Consequently, users are able to explore their data by interactively specifying the spatial and temporal windows that define encounters. We evaluate our proposed method as a function of its input parameters and data size. We instantiate the proposed method on urban public transportation data, where we found a large number of encounters. We show that single encounters emerge into higher level patterns that are of particular interest and value to the domain.

Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration

We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data.

Algorithmic and visual analysis of spatiotemporal stops in movement data

Analyzing the occurrence of stops in transportation systems is an important challenge to better understand traffic congestion problems and find corresponding solutions. We propose an efficient system to analyze stop occurrences. It consists of two major parts: (1) an efficient clustering algorithm to partition the stops into groups based on strongly connected components (2) an interactive visual representation of the results to provide insights to domain experts.

Visual analytics for movement behavior in traffic and transportation

movement behavior in traffic and transportation P. Bak H. Ship A. Yaeli Y. Nardi E. Packer G. Saadoun J. Bnayahu L. Peterfreund Understanding movement of vehicles, people, goods, or any type of object is important for making knowledgeable decisions regarding public transportation planning. However, movement is a complex and dynamic phenomenon, and until recently, movement data was difficult to exploit for such planning purposes. The widespread adoption of location-aware devices such as Global Positioning System (GPS) sensors in public transportation systems and the adoption of open data principles have set the stage for new methods and tools for data collection and analysis of movement patterns. This paper illustrates the value and benefit of applying visual analytics techniques to movement data to create valuable insight for public transportation planning using vehicle-mounted devices on buses and trams. The contribution of the paper is three distinct visual analytics solutions that we developed using a real-world open data feed published by the Helsinki Public Transport Authority. The current work addresses encounters between objects, stops that interrupt movement, and flow dynamics of a large number of moving objects. We instantiated the described methods by showing that our findings can be applied in real-world use cases.