SPAM

The tremendous increase in spatial database technology has created furious interest among the researchers to adopt new methodologies for discovery of interesting patterns among large databases. But the raw data gathered from various resources such as Geographic Information Systems (GIS), business organizations, medical databases, climatic, market survey, remote sensing and several other resources might consist of data, which can be relevant, irrelevant or noisy in nature. However, retrieval of patterns from such databases can lead to serious concerns, which can frame inconsistent or irrelevant futuristic results. To deal with such issues, feature selection techniques are adopted to remove irrelevant, redundant and noisy features. Our approach focuses on retrieval of effective and efficient spatial clusters from large number of medical databases. In this chapter, we have defined our novel framework SpaGrid and SPAM algorithm to retrieve clusters of variant shape and size from large databases. The application of our framework is used with spatial medical databases where the implementation details are discussed with Matlab 7.1.

Care to Share?

GPS technology has been extensively used to optimize operation of taxi systems since the first appearance of commercial GPS devices. Owing to this, data sets generated by taxi fleets are amongst the first and most representative examples of massive GPS data that have been systematically collected. The analysis of these data sets has recently generated a rich literature aimed at, among other things, identifying optimal taxi driver strategies, predicting taxi demand or location of vacant taxis, etc. This chapter focuses on what is a new, exciting field of investigation of GPS taxi data analysis, namely, evaluating the impact of a shared taxi system on the urban environment. After introducing the notion of (taxi) ride sharing, the chapter presents the relevant literature, describing in greater details a methodological approach called “shareability network” that allows formal characterization of taxi sharing opportunities in an urban environment.

The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects

The current phenomenon of Big Data – the use of datasets that are too big for traditional business analysis tools used in industry – is driving a shift in how social and economic problems are understood and analysed. This chapter explores the role Big Data can play in analysing the effectiveness of crowd-funding projects, using the data from such a project, which aimed to fund the development of a software plug-in called ‘QPress'. Data analysed included the website metrics of impressions, clicks and average position, which were found to be significantly connected with geographical factors using an ANOVA. These were combined with other country data to perform t-tests in order to form a geo-demographic understanding of those who are displayed advertisements inviting participation in crowd-funding. The chapter concludes that there are a number of interacting variables and that for Big Data studies to be effective, their amalgamation with other data sources, including linked data, is essential to providing an overall picture of the social phenomenon being studied.

Assessing Financial Well-Being of Merchants by Analyzing Behavioral Patterns in Historical Transactions

This study focuses on a new approach to estimate financial wellbeing indicators for merchants, by looking at behavioral patterns of their customers using transaction history. The transaction data for about 10,000 merchants in a specific country, was analyzed in terms of diversity and propensity of their customers using factors like age, distance travelled to shop, time of the day for shopping, day of the week for shopping, educational status, gender etc. These factors were used as independent variables to predict the financial well-being of merchants, particularly in two dimensions – total revenue and consistency in revenue, both relative to other merchants in the same industry. The merchants were then also divided into the categories of Essential, Non- essential and Luxury goods depending on the industry they belong to and it was interesting to observe the contrast across categories. The results suggest that behavioral patterns could be used to augment current methods of calculating credit score.

Developing a Method for Visualizing Population Movements

This chapter describes a technique for visualizing the movements of a population in a region at a point in time. It is suitable for cases where a large population is spread throughout the region and can move in all directions, for example the population of a large city. By repeatedly clustering movement vector arrows it can visually summarize the movements of millions of individuals, and do so with moderate computing resources. The technique is designed to work with data captured from mobile phone networks, but other sources of data can also be used.

Analysis of Mobile Phone Call Data of Istanbul Residents

In this chapter, we analyze call detail records of subscribers of a major cellular network provider in Turkey with a focus on subscribers that reside in Istanbul. We consider a sample of 10,000 opt-in subscribers, chosen proportionally to the population density of each district of Istanbul. The anonymized cell phone usage data for 6 weeks are combined with demographic and subscription package attributes. Our methodology consists of data retrieval and cleaning, analysis and visualization. The analysis aims to extract information to be used mainly in disaster preparedness, marketing and public service design, and is categorized under: 1) understanding call habits in terms of call duration and call location with respect to gender and age categories, 2) tracking population density changes by time and district, 3) segmentation of people visiting specified locations, 4) information on mobility of disabled subscribers, and 5) international travel patterns by roaming data analysis.

Building a Visual Analytics Tool for Location-Based Services

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Campaign Optimization through Mobility Network Analysis

Optimizing the use of available resources is one of the key challenges in activities that consist of interactions with a large number of “target individuals”, with the ultimate goal of affecting as many of them as possible, such as in marketing, service provision and political campaigns. Typically, the cost of interactions is monotonically increasing such that a method for maximizing the performance of these campaigns is required. This chapter proposes a mathematical model to compute an optimized campaign by automatically determining the number of interacting units and their type, and how they should be allocated to different geographical regions in order to maximize the campaign's performance. The proposed model is validated using real world mobility data.

Data Visualization Using Weighted Voronoi Diagrams

A Voronoi diagram is a standard spatial tessellation that partitions a domain into sub-regions based on proximity to a fixed set of landmark points. In order to maintain control over the size and shape of these sub-regions, a weighting scheme is often used, in which each landmark has a scalar value associated with it. This suggests a natural “inverse” problem: given a fixed set of landmark points in a given planar region and a set of “desired” areas, is it possible to calculate a set of weights so that each sub-region has a particular area? In this chapter, the authors give a fast scheme for determining these weights based on theory from convex optimization, which is then applied to a variety of problems in data visualization.

Demystifying Big Data in the Cloud

Big data in the cloud are an emerging paradigm for huge and federated data processing, storing and distributing by deploying web applications. Scalability, elasticity, pay-per-use pricing, and an advance of ICT scale from large and dynamic applications and performance are the major reasons for the success and widespread adoption of big data cloud infrastructures. It is ‘no secret of the enterprise data', which is challenging for privacy and security. In this chapter, authors deeply discussed and introduce novel approaches and methodologies to easily understood big data phenomenon and technology towards data or web resources privacy and security. Nutshell, big data has a powerful potential to predict cloud risks to develop and deploy corporate security strategies. The chapter's contribution is, in general, to gain a meaningful insight of big data in the cloud and its applications, which is hot issues for today's businesses to make proactive and knowledge-driven decisions.