In terms of public services, governments do not yet know how to treat users as different and unique individuals. At worst, users are still considered an undifferentiated mass, or at best as segments. However, the benefits of universal personalisation in public services are within reach technologically through e-government developments. Universal personalisation will involve achieving a balance between top-down government- and data-driven services, on the one hand, and bottom-up self-directed and user-driven services on the other. There are at least three main technological, organisational and societal drivers. First, top-down data-driven, often automatic, services based on the huge data resources available in the cloud and the technologies enabling the systematic exploitation of these by governments. Second, increasing opportunities for users themselves or their intermediaries to select or create their own service environments, bottom-up, through ‘user-driven’ services, drawing directly on the data cloud. Third, a move to ‘everyday’, location-driven e-government based largely on mobile smart phones using GPS and local data clouds, where public services are offered depending on where people are as well as who they are and what they are doing. This paper examines practitioners and researchers and describes model current trends based on secondary research and literature review.
Data-driven deep density estimation