Urban Sprawl Pattern Analysis Using GIS

Urbanisation has evinced interest from a wide section of the society including experts, amateurs, and novices. The multidisciplinary scope of the subject invokes the interest from ecologists, to urban planners and civil engineers, to sociologists, to administrators and policy makers, students and finally the common man. With the development and infrastructure initiatives mostly around the urban centres, the impacts of urbanisation and sprawl would be on the environment and the natural resources. The wisdom lies in how effectively we plan the urban growth without - hampering the environment, excessively harnessing the natural resources and eventually disturbing the natural set-up. The research on these help urban residents and policymakers make informed decisions and take action to restore these resources before they are lost. Ultimately the power to balance the urban ecosystems rests with regional awareness, policies, administration practices, management issues and operational problems. This publication on urban systems is aimed at helping scientists, policy makers, engineers, urban planners and ultimately the common man to visualise how towns and cities grow over a period of time based on investigations in the regions around the highway and cities. Two important highways in Karnataka, South India, viz., Bangalore - Mysore highway and the Mangalore - Udupi highway, in Karnataka and the Tiruchirapalli - Tanjavore - Kumbakonam triangular road network in Tamil Nadu, South India, were considered in this investigation. Geographic Information System and Remote Sensing data were used to analyse the pattern of urbanisation. This was coupled with the spatial and temporal data from the Survey of India toposheets (for 1972), satellite imageries procured from National Remote Sensing Agency (NRSA) (LANDSAT TM for 1987 and IRS LISS III for 1999), demographic details from the Census of India (1971, 1981, 1991 and 2001) and the village maps from the Directorate of Survey Settlements and Land Records, Government of Karnataka. All this enabled in quantifying the increase in the built-up area for nearly three decades. With intent of identifying the potential sprawl zones, this could be modelled and projected for the future decades. Apart from these the study could quantify some of the metrics that could be used in the study of urban sprawl.

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

Virtually cool ternary content addressable memory

Fast content addressable data access mechanisms have compelling applications in today's systems. Many of these exploit the powerful wildcard matching capabilities provided by ternary content addressable memories. For example, TCAM based implementations of important algorithms in data mining been developed in recent years; these achieve an an order of magnitude speedup over prevalent techniques. However, large hardware TCAMs are still prohibitively expensive in terms of power consumption and cost per bit. This has been a barrier to extending their exploitation beyond niche and special purpose systems. We propose an approach to overcome this barrier by extending the traditional virtual memory hierarchy to scale up the user visible capacity of TCAMs while mitigating the power consumption overhead. By exploiting the notion of content locality (as opposed to spatial locality), we devise a novel combination of software and hardware techniques to provide an abstraction of a large virtual ternary content addressable space. In the long run, such abstractions enable applications to disassociate considerations of spatial locality and contiguity from the way data is referenced. If successful, ideas for making content addressability a first class abstraction in computing systems can open up a radical shift in the way applications are optimized for memory locality, just as storage class memories are soon expected to shift away from the way in which applications are typically optimized for disk access locality.

Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.

Wind speed prediction using spatio-temporal covariance

High wind poses a number of hazards in different areas such as structural safety, aviation, and wind energy-where low wind speed is also a concern, pollutant transport, to name a few. Therefore, usage of a good prediction tool for wind speed is necessary in these areas. Like many other natural processes, behavior of wind is also associated with considerable uncertainties stemming from different sources. Therefore, to develop a reliable prediction tool for wind speed, these uncertainties should be taken into account. In this work, we propose a probabilistic framework for prediction of wind speed from measured spatio-temporal data. The framework is based on decompositions of spatio-temporal covariance and simulation using these decompositions. A novel simulation method based on a tensor decomposition is used here in this context. The proposed framework is composed of a set of four modules, and the modules have flexibility to accommodate further modifications. This framework is applied on measured data on wind speed in Ireland. Both short-and long-term predictions are addressed.