A Spatial Planning Support System for Managing Bangalore's Urban Sprawl

Urban sprawl is the outgrowth along the periphery of cities and along highways. Although an accurate definition of urban sprawl may be debated, a consensus is that urban sprawl is characterized by an unplanned and uneven pattern of growth, driven by multitude of processes and leading to inefficient resource utilization. Urbanization in India has never been as rapid as it is in recent times. As one of the fastest growing economies in the world, India faces stiff challenges in managing the urban sprawl, while ensuring effective delivery of basic services in urban areas. The urban areas contribute significantly to the national economy (more than 50% of GDP), while facing critical challenges in accessing basic services and necessary infrastructure, both social and economic. The overall rise in the population of the urban poor or the increase in travel times due to congestion along road networks are indicators of the effectiveness of planning and governance in assessing and catering for this demand. Agencies of governance at all levels: local bodies, state government and federal government, are facing the brunt of this rapid urban growth. It is imperative for planning and governance to facilitate, augment and service the requisite infrastructure over time systematically. Provision of infrastructure and assurance of the delivery of basic services cannot happen overnight and hence planning has to facilitate forecasting and service provision with appropriate financial mechanisms.

Spatial ecology of an oak-associated herbivore community : A metacommunity of herbivores

Habitat fragmentation is currently affecting many species throughout the world. As a consequence, an increasing number of species are structured as metapopulations, i.e. as local populations connected by dispersal. While excellent studies of metapopulations have accumulated over the past 20 years, the focus has recently shifted from single species to studies of multiple species. This has created the concept of metacommunities, where local communities are connected by the dispersal of one or several of their member species. To understand this higher level of organisation, we need to address not only the properties of single species, but also establish the importance of interspecific interactions. However, studies of metacommunities are so far heavily biased towards laboratory-based systems, and empirical data from natural systems are urgently needed. My thesis focuses on a metacommunity of insect herbivores on the pedunculate oak Quercus robur a tree species known for its high diversity of host-specific insects. Taking advantage of the amenability of this system to both observational and experimental studies, I quantify and compare the importance of local and regional factors in structuring herbivore communities. Most importantly, I contrast the impact of direct and indirect competition, host plant genotype and local adaptation (i.e. local factors) to that of regional processes (as reflected by the spatial context of the local community). As a key approach, I use general theory to generate testable hypotheses, controlled experiments to establish causal relations, and observational data to validate the role played by the pinpointed processes in nature. As the central outcome of my thesis, I am able to relegate local forces to a secondary role in structuring oak-based insect communities. While controlled experiments show that direct competition does occur among both conspecifics and heterospecifics, that indirect interactions can be mediated by both the host plant and the parasitoids, and that host plant genotype may affect local adaptation, the size of these effects is much smaller than that of spatial context. Hence, I conclude that dispersal between habitat patches plays a prime role in structuring the insect community, and that the distribution and abundance of the target species can only be understood in a spatial framework. By extension, I suggest that the majority of herbivore communities are dependent on the spatial structure of their landscape and urge fellow ecologists working on other herbivore systems to either support or refute my generalization.

Spatial correlation in grain misorientation distribution

Grain misorientation was studied in relation to the nearest neighbor's mutual distance using electron back-scattered diffraction measurements. The misorientation correlation function was defined as the probability density for the occurrence of a certain misorientation between pairs of grains separated by a certain distance. Scale-invariant spatial correlation between neighbor grains was manifested by a power law dependence of the preferred misorientation vs. inter-granular distance in various materials after diverse strain paths. The obtained negative scaling exponents were in the range of -2 +/- 0.3 for high-angle grain boundaries. The exponent decreased in the presence of low-angle grain boundaries or dynamic recrystallization, indicating faster decay of correlations. The correlations vanished in annealed materials. The results were interpreted in terms of lattice incompatibility and continuity conditions at the interface between neighboring grains. Grain-size effects on texture development, as well as the implications of such spatial correlations on texture modeling, were discussed.

Development and evaluation of a spatial decision support system for malaria elimination in Bhutan

Background: Bhutan has reduced its malaria incidence significantly in the last 5 years, and is aiming for malaria elimination by 2016. To assist with the management of the Bhutanese malaria elimination programme a spatial decision support system (SDSS) was developed. The current study aims to describe SDSS development and evaluate SDSS utility and acceptability through informant interviews. Methods: The SDSS was developed based on the open-source Quantum geographical information system (QGIS) and piloted to support the distribution of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) in the two sub-districts of Samdrup Jongkhar District. It was subsequently used to support reactive case detection (RACD) in the two sub-districts of Samdrup Jongkhar and two additional sub-districts in Sarpang District. Interviews were conducted to ascertain perceptions on utility and acceptability of 11 informants using the SDSS, including programme and district managers, and field workers. Results: A total of 1502 households with a population of 7165 were enumerated in the four sub-districts, and a total of 3491 LLINs were distributed with one LLIN per 1.7 persons. A total of 279 households representing 728 residents were involved with RACD. Informants considered that the SDSS was an improvement on previous methods for organizing LLIN distribution, IRS and RACD, and could be easily integrated into routine malaria and other vector-borne disease surveillance systems. Informants identified some challenges at the programme and field level, including the need for more skilled personnel to manage the SDSS, and more training to improve the effectiveness of SDSS implementation and use of hardware. Conclusions: The SDSS was well accepted and informants expected its use to be extended to other malaria reporting districts and other vector-borne diseases. Challenges associated with efficient SDSS use included adequate skills and knowledge, access to training and support, and availability of hardware including computers and global positioning system receivers.

Spatial and vertical heterogeneities in aerosol properties over oceanic regions around India: Implications for radiative forcing

The influence of atmospheric aerosols on Earth's radiation budget and hence climate, though well recognized and extensively investigated in recent years, remains largely uncertain mainly because of the large spatio-temporal heterogeneity and the lack of data with adequate resolution. To characterize this diversity, a major multi-platform field campaign ICARB (Integrated Campaign for Aerosols, gases and Radiation Budget) was carried out during the pre-monsoon period of 2006 over the Indian landmass and surrounding oceans, which was the biggest such campaign ever conducted over this region. Based on the extensive and concurrent measurements of the optical and physical properties of atmospheric aerosols during ICARB, the spatial distribution of aerosol radiative forcing was estimated over the entire Bay of Bengal (BoB), northern Indian Ocean and Arabian Sea (AS) as well as large spatial variations within these regions. Besides being considerably lower than the mean values reported earlier for this region, our studies have revealed large differences in the forcing components between the BoB and the AS. While the regionally averaged aerosol-induced atmospheric forcing efficiency was 31 +/- 6 W m(-2) tau(-1) for the BoB, it was only similar to 18 +/- 7 W m(-2) tau(-1) for the AS. Airborne measurements revealed the presence of strong, elevated aerosol layers even over the oceans, leading to vertical structures in the atmospheric forcing, resulting in significant warming in the lower troposphere. These observations suggest serious climate implications and raise issues ranging from the impact of aerosols on vertical thermal structure of the atmospheric and hence cloud formation processes to monsoon circulation.

Influence of spatial variability of permeability property on steady state seepage flow and slope stability analysis

In recent years, spatial variability modeling of soil parameters using random field theory has gained distinct importance in geotechnical analysis. In the present Study, commercially available finite difference numerical code FLAC 5.0 is used for modeling the permeability parameter as spatially correlated log-normally distributed random variable and its influence on the steady state seepage flow and on the slope stability analysis are studied. Considering the case of a 5.0 m high cohesive-frictional soil slope of 30 degrees, a range of coefficients of variation (CoV%) from 60 to 90% in the permeability Values, and taking different values of correlation distance in the range of 0.5-15 m, parametric studies, using Monte Carlo simulations, are performed to study the following three aspects, i.e., (i) effect ostochastic soil permeability on the statistics of seepage flow in comparison to the analytic (Dupuit's) solution available for the uniformly constant permeability property; (ii) strain and deformation pattern, and (iii) stability of the given slope assessed in terms of factor of safety (FS). The results obtained in this study are useful to understand the role of permeability variations in slope stability analysis under different slope conditions and material properties. (C) 2009 Elsevier B.V. All rights reserved.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Memory-efficient spatial prediction image compression scheme

In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates the predicted error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. In quantization phase, we used a modified SPIHT algorithm to achieve efficiency in memory requirements. The memory constraint plays a vital role in wireless and bandwidth-limited applications. A single reusable list is used instead of three continuously growing linked lists as in case of SPIHT. This method is error resilient. The performance is measured in terms of PSNR and memory requirements. The algorithm shows good compression performance and significant savings in memory. (C) 2006 Elsevier B.V. All rights reserved.

Towards the Use of Radar Winds in Numerical Weather Prediction

Numerical weather prediction (NWP) models provide the basis for weather forecasting by simulating the evolution of the atmospheric state. A good forecast requires that the initial state of the atmosphere is known accurately, and that the NWP model is a realistic representation of the atmosphere. Data assimilation methods are used to produce initial conditions for NWP models. The NWP model background field, typically a short-range forecast, is updated with observations in a statistically optimal way. The objective in this thesis has been to develope methods in order to allow data assimilation of Doppler radar radial wind observations. The work has been carried out in the High Resolution Limited Area Model (HIRLAM) 3-dimensional variational data assimilation framework. Observation modelling is a key element in exploiting indirect observations of the model variables. In the radar radial wind observation modelling, the vertical model wind profile is interpolated to the observation location, and the projection of the model wind vector on the radar pulse path is calculated. The vertical broadening of the radar pulse volume, and the bending of the radar pulse path due to atmospheric conditions are taken into account. Radar radial wind observations are modelled within observation errors which consist of instrumental, modelling, and representativeness errors. Systematic and random modelling errors can be minimized by accurate observation modelling. The impact of the random part of the instrumental and representativeness errors can be decreased by calculating spatial averages from the raw observations. Model experiments indicate that the spatial averaging clearly improves the fit of the radial wind observations to the model in terms of observation minus model background (OmB) standard deviation. Monitoring the quality of the observations is an important aspect, especially when a new observation type is introduced into a data assimilation system. Calculating the bias for radial wind observations in a conventional way can result in zero even in case there are systematic differences in the wind speed and/or direction. A bias estimation method designed for this observation type is introduced in the thesis. Doppler radar radial wind observation modelling, together with the bias estimation method, enables the exploitation of the radial wind observations also for NWP model validation. The one-month model experiments performed with the HIRLAM model versions differing only in a surface stress parameterization detail indicate that the use of radar wind observations in NWP model validation is very beneficial.

Evaluation and modelling of the spatial and temporal variability of particulate matter in urban areas

This thesis contains three subject areas concerning particulate matter in urban area air quality: 1) Analysis of the measured concentrations of particulate matter mass concentrations in the Helsinki Metropolitan Area (HMA) in different locations in relation to traffic sources, and at different times of year and day. 2) The evolution of traffic exhaust originated particulate matter number concentrations and sizes in local street scale are studied by a combination of a dispersion model and an aerosol process model. 3) Some situations of high particulate matter concentrations are analysed with regard to their meteorological origins, especially temperature inversion situations, in the HMA and three other European cities. The prediction of the occurrence of meteorological conditions conducive to elevated particulate matter concentrations in the studied cities is examined. The performance of current numerical weather forecasting models in the case of air pollution episode situations is considered. The study of the ambient measurements revealed clear diurnal variation of the PM10 concentrations in the HMA measurement sites, irrespective of the year and the season of the year. The diurnal variation of local vehicular traffic flows seemed to have no substantial correlation with the PM2.5 concentrations, indicating that the PM10 concentrations were originated mainly from local vehicular traffic (direct emissions and suspension), while the PM2.5 concentrations were mostly of regionally and long-range transported origin. The modelling study of traffic exhaust dispersion and transformation showed that the number concentrations of particles originating from street traffic exhaust undergo a substantial change during the first tens of seconds after being emitted from the vehicle tailpipe. The dilution process was shown to dominate total number concentrations. Minimal effect of both condensation and coagulation was seen in the Aitken mode number concentrations. The included air pollution episodes were chosen on the basis of occurrence in either winter or spring, and having at least partly local origin. In the HMA, air pollution episodes were shown to be linked to predominantly stable atmospheric conditions with high atmospheric pressure and low wind speeds in conjunction with relatively low ambient temperatures. For the other European cities studied, the best meteorological predictors for the elevated concentrations of PM10 were shown to be temporal (hourly) evolutions of temperature inversions, stable atmospheric stability and in some cases, wind speed. Concerning the weather prediction during particulate matter related air pollution episodes, the use of the studied models were found to overpredict pollutant dispersion, leading to underprediction of pollutant concentration levels.

The effects of marriage partners' socio-economic positions on the risk of divorce in Finland

Väitöskirjatutkimuksen tavoitteena oli selvittää, miten aviopuolisoiden sosioekonominen asema vaikuttaa avioeroriskiin Suomessa. Tutkimuksessa käytettiin Tilastokeskuksen rekistereistä koottua aineistoa, joka koskee suomalaisten ensimmäisiä avioliittoja vuoden 1990 lopussa ja avioeroja vuosina 1991−93. Väitöskirjaan sisältyy kolme osatutkimusta. Ensimmäinen osatutkimus käsitteli avioeroriskin vaihtelua aviopuolisoiden sosioekonomisen aseman eri osatekijöiden (koulutusaste, sosiaaliryhmä, pääasiallinen toiminta, tulotaso, asunnon omistaminen ja asumisahtaus) mukaan. Kaiken kaikkiaan avioeroriski oli sitä pienempi, mitä paremmassa taloudellisessa ja sosiaalisessa asemassa aviopuolisot olivat. Esimerkiksi miehen ja vaimon korkea koulutusaste, toimihenkilöammatti, työssäkäynti (etenkin verrattuna työttömyyteen) sekä omistusasunnossa asuminen liittyivät pienentyneeseen avioeroriskiin. Vaimon sosioekonomisen aseman yhteys avioeroriskiin oli paljolti samanlainen kuin miehen aseman yhteys. Huomattavin poikkeus tähän oli, että vaimon suuret tulot lisäsivät avioeroriskiä, vaikka miehen suurilla tuloilla oli päinvastainen vaikutus. Lisäksi kotitaloustyötä pääasiallisena toimintanaan tekevillä naisilla (pääasiallisen toiminnan luokka ”muut”) oli vielä pienempi avioeroriski kuin työssäkäyvillä naisilla. Toisessa osatutkimuksessa keskityttiin aviomiehen ja vaimon aseman yhdistettyyn vaikutukseen. Selviä viitteitä siitä, että puolisoiden koulutustasojen erilaisuus lisäisi eroriskiä, ei saatu. Pareilla, joissa molemmilla oli enintään perusasteen koulutus, oli kuitenkin odotettua pienempi avioeroriski. Eroriski oli suhteellisen alhainen pareilla, joissa vaimo oli työssäkäyvä tai kotitaloustyötä tekevä ja aviomies työssäkäyvä. Eroriskiä kasvatti se, että aviomies, vaimo tai molemmat puolisot olivat työttömiä. Vaimon korkea tulotaso lisäsi eroriskiä miehen kaikilla tulotasoilla mutta erityisen voimakkaasti silloin, kun miehen tulotaso oli alhainen. Kolmanneksi selvitettiin, vaikuttaako puolisoiden sosioekonominen asema avioeroriskiin eri tavalla riippuen siitä, kauanko avioliitto on kestänyt. Tällöin havaittiin, että vähän koulutettujen ja työntekijäammateissa toimivien puolisoiden suuri eroriski rajoittuu paljolti nuorimpiin avioliittoihin. Sen sijaan esim. puolisoiden työttömyys, vaimon korkea tulotaso ja vuokra-asunnossa asuminen kasvattivat eroriskiä riippumatta siitä, kuinka kauan avioliitto oli kestänyt. Kaiken kaikkiaan eroriski oli siis sitä pienempi, mitä paremmassa taloudellisessa ja sosiaalisessa asemassa puolisot olivat. Vaimon taloudellisilla ja sosiaalisilla resursseilla näyttää kuitenkin olevan myös joitakin avioeroriskiä lisääviä vaikutuksia.

Glycosphingolipids in microdomain formation and their spatial organization

Plasma membranes regulate the influx and efflux of molecules across themselves and are also responsible for primary signal transduction between cells or within the same cell. Presence of lateral heterogeneity and the ability of reorganization are essential requirements for effective functioning of biomembranes. Lipid rafts are small, heterogeneous, dynamic domains enriched in glycosphingolipids, sphingomyelin and cholesterol, and profoundly influence membrane organization. Glycosphingolipids are inclined towards formation of liquid-ordered phases in membranes, both with and without cholesterol; they are therefore prime players in domain formation. Here, we discuss the role of glycosphingolipids in microdomain formation and their spatial organization within these rafts.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.