Sosiaaliturvan ulkopuolella - kokemuksia kansalaisuuden reunalta

Muuttaessaan maasta toiseen ihminen kohtaa useita rajoja. Ylittäessään kohdemaan valtion rajan hän kulkee läpi ensimmäisestä maahanmuuton portista. Toinen raja erottaa tilapäiset asukkaat pysyvistä: tämän maahanmuuton toisen portin läpikulkemisen myötä yksilö pääsee osalliseksi sosiaalisista oikeuksista. Maahanmuuton viimeisestä portista kuljettuaan yksilö saavuttaa kyseisen valtion kansalaisuuden. (Hammar 1990, 21.) Tässä pro gradu -tutkielmassa tarkastelen toisen maahanmuuton portin aukeamista ja sosiaaliturvan piiriin pääsyä odottavien maahan-muuttajien kokemuksia. Käytän tarkastelussa sosiaalisen kansalaisuuden ja marginaalisuuden käsitteitä. Tutkielmassa selvitän, miten sosiaali- ja terveyspalveluiden sekä toimeentuloturvan ulkopuolelle jääminen vaikuttaa maahanmuuttajien arkeen ja miten he kokevat osallisuutensa ja jäsenyytensä yhteiskunnassa. Tutkimus on lähtökohdiltaan fenomenologis-hermeneuttinen ja sovellan lähestymistapana ko-kemuksiin keskittyvän narratiivista tutkimusta. Tutkimusaineisto on koottu kevään 2011 aikana ja se koostuu 10 teemahaastattelusta. Haastateltavien maahanmuuton keinot ja syyt vaihtelivat: he olivat saapuneet Suomeen perhesyistä, työn vuoksi tai hakeakseen turvaa. Haastateltavat tavoitettiin Helsingin Diakoniaopiston, Pro-tukipisteen, Kansainvälisen seurakunnan ja tuttava-verkostojen kautta. Aineiston analyysi toteutettiin sisällönanalyysillä Atlas-ohjelman avulla syksyn 2011 aikana. Toisen maahanmuuton portin aukeamisen odottaminen oli raskaaksi: tuota aikaa leimasi epä-varmuus, tyhjyys ja yksinäisyys. Sosiaaliturvan ulkopuolella jääminen aiheutti osalle haastatel-tavista taloudellisia vaikeuksia sekä ongelmia terveydenhuollon palveluiden piiriin pääsemisessä. Toisaalta apua hakeneet haastateltavat olivat sitä lopulta saaneet. Auttamistyön ammattilaiset ja maistraatti saivat haastateltavien kertomuksissa portinvartijan aseman. Kaikille sosiaaliturvan ulkopuolelle jääminen ei ollut ongelma vaan he kokivat sosiaaliturvan puutetta suuremmaksi ongelmaksi työnteko-oikeuden puuttumisen. Kuulumisen ja ulkopuolisuuden kokemus voivat olla läsnä samanaikaisesti, ja kuulumisesta neuvotellaan jatkuvasti esimerkiksi sosiaalisessa kanssakäymisessä tai palveluita hakiessa. Insti-tutionaaliset käytännöt ja poiskäännyttämisen kokemukset tuottavat marginaalisia identiteettejä. Tasavertainen oikeus sosiaaliturvaan vahvistaa kokemusta kuulumisesta ja kodista. Sosiaaliturva ei kuitenkaan yksin määritä kuulumisen ja kodin kokemusta vaan siihen vaikuttavat myös muut tekijät. Näistä tärkeimmät ovat kehon fyysinen sijoittuminen Suomeen, perhe- ja ystä-vyyssuhteet, työ, asunto ja rasismin kokemukset.

Elämää keskustassa ja kaupunkiseudun reunoilla. Urbaani ja jälkiesikaupungillinen elämäntyyli asumisen valinnoissa ja arkiliikkumisessa Helsingin seudulla.

The lifestyles of people living in single-family housing areas on the outskirts of the Greater Helsinki Region (GHR) are different from those living in inner city area. The urban structure of the GHR is concentrated in the capital on the one hand, and spread out across the outskirts on the other. Socioeconomic spatial divisions are evident as well-paid and educated residents move to the inner city or the single-family house dominated suburban neighbourhoods depending on their housing preferences and life situations. The following thesis explores how these lifestyles have emerged through the housing choices and daily mobility of the residents living in the new single-family housing areas on the outskirts of the GHR and the inner city. The study shows that, when it comes to lifestyles, residents on the outskirts of the region have different housing preferences and daily mobility patterns when compared with their inner city counterparts. Based on five different case study areas my results show that these differences are related to residents values, preferences and attitudes towards the neighbourhood, on the one hand, and limited by urban structure on the other. This also confirms earlier theoretical analyses and findings from the GHR. Residents who moved to the outskirts of Greater Helsinki Region and the apartment buildings of the inner city were similar in the basic elements of their housing preferences: they sought a safe and peaceful neighbourhood close to the natural environment. However, where housing choices, daily mobility and activities vary different lifestyles develop in both the outskirts and the inner city. More specifically, lifestyles in the city apartment blocks were inherently urban. Liveliness and highest order facilities were appreciated and daily mobility patterns were supported by diverse modes of transportation for the purposes of work, shopping and leisure time. On the outskirts, by contrast, lifestyles were largely post-suburban and child-friendliness appreciated. Due to the heterachical urban structure, daily mobility was more car-dependent since work, shopping and free time activities of the residents are more spread around the region. The urban structure frames the daily mobility on the outskirts of the region, but this is not to say that short local trips replace longer regional ones. This comparative case study was carried out in the single-family housing areas of Sundsberg in Kirkkonummi, Landbo in Helsinki and Ylästö in Vantaa, as well as in the inner city apartment building areas of Punavuori and Katajanokka in Helsinki. The data is comprised of residential surveys, interviews, and statistics and GIS data sets that illustrate regional daily mobility, socio-economic structure and vis-à-vis housing stock.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R (H) in a river basin at monthly scale. Uncertainty in the future projections of R (H) is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R (H) are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978-2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978-2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R (H) is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

Simulating the daily discharge of the Mandovi River, west coast of India

A hydrological modelling framework was assembled to simulate the daily discharge of the Mandovi River on the Indian west coast. Approximately 90% of the west-coast rainfall, and therefore discharge, occurs during the summer monsoon (June-September), with a peak during July-August. The modelling framework consisted of a digital elevation model (DEM) called GLOBE, a hydrological routing algorithm, the Terrestrial Hydrological Model with Biogeochemistry (THMB), an algorithm to map the rainfall recorded by sparse rain-gauges to the model grid, and a modified Soil Conservation Service Curve Number (SCS-CN) method. A series of discharge simulations (with and without the SCS method) was carried out. The best simulation was obtained after incorporating spatio-temporal variability in the SCS parameters, which was achieved by an objective division of the season into five regimes: the lean season, monsoon onset, peak monsoon, end-monsoon, and post-monsoon. A novel attempt was made to incorporate objectively the different regimes encountered before, during and after the Indian monsoon, into a hydrological modelling framework. The strength of our method lies in the low demand it makes on hydrological data. Apart from information on the average soil type in a region, the entire parameterization is built on the basis of the rainfall that is used to force the model. That the model does not need to be calibrated separately for each river is important, because most of the Indian west-coast basins are ungauged. Hence, even though the model has been validated only for the Mandovi basin, its potential region of application is considerable. In the context of the Prediction in Ungauged Basins (PUB) framework, the potential of the proposed approach is significant, because the discharge of these (ungauged) rivers into the eastern Arabian Sea is not small, making them an important element of the local climate system.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system’s Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system's Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

A quinazoline derivative as quick-response red-shifted reporter for nanomolar Al3+ and applicable to living cell staining

A newly synthesized and structurally characterized quinazoline derivative (L) has been shown to act as a quick-response chemosensor for Al3+ with a high selectivity over other metal ions in water-DMSO. In the presence of Al3+, L shows a red-shifted ratiometric enhancement in fluorescence as a result of internal charge transfer and chelation-enhanced fluorescence through the inhibition of a photo-induced electron transfer mechanism. This probe detects Al3+ at concentrations as low as 1.48 nM in 100 mM HEPES buffer (DMSO-water, 1 : 9 v/v) at biological pH with a very short response time (15-20 s). L was applied to biological imaging to validate its utility as a fluorescent probe for monitoring Al3+ ions in living cells, illustrating its value in practical environmental and biological systems.

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5A degrees x 2.5A degrees grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO - June to October), non-monsoon season (JFMAMND - January to May, November, December) and for the entire year (''Annual''). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5A degrees grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

A rhodamine-based `turn-on' Al3+ ion-selective reporter and the resultant complex as a secondary sensor for F- ion are applicable to living cell staining

A newly designed fluorescent aluminum(III) complex (L'-Al; 2) of a structurally characterized non-fluorescent rhodamine Schiff base (L) has been isolated in pure form and characterized using spectroscopic and physico-chemical methods with theoretical density functional theory (DFT) support. On addition of Al(III) ions to a solution of L in HEPES buffer (1 mM, pH 7.4; EtOH-water, 1 : 3 v/v) at 25 degrees C, the systematic increase in chelation-enhanced fluorescence (CHEF) enables the detection of Al(III) ions as low as 60 nM with high selectivity, unaffected by the presence of competitive ions. Interestingly, the Al(III) complex (L'-Al; 2) is specifically able to detect fluoride ions by quenching the fluorescence in the presence of large amounts of other anions in the HEPES buffer (1 mM, pH 7.4) at 25 degrees C. On the basis of our experimental and theoretical findings, the addition of Al3+ ions to a solution of L helps to generate a new fluorescence peak at 590 nm, due to the selective binding of Al3+ ions with L in a 1 : 1 ratio with a binding constant (K) of 8.13 x 10(4) M-1. The Schiff base L shows no cytotoxic effect, and it can therefore be employed for determining the intracellular concentration of Al3+ and F-ions by 2 in living cells using fluorescence microscopy.