Research Shows the Cost Benefits of Resident Services on the Performance of Property Operations

In the past, anecdotal evidence has suggested that resident services in affordable family housing help reduce operational costs. This is the third study to support and validate that anecdotal evidence with concrete data. The study found that resident services reduce operational costs related to vacancy loss, bad debt and legal fees

Internal and external potential-field estimation from regional vector data at varying satellite altitude

When modeling global satellite data to recover a planetary magnetic or gravitational potential field and evaluate it elsewhere, the method of choice remains their analysis in terms of spherical harmonics. When only regional data are available, or when data quality varies strongly with geographic location, the inversion problem becomes severely ill-posed. In those cases, adopting explicitly local methods is to be preferred over adapting global ones (e.g., by regularization). Here, we develop the theory behind a procedure to invert for planetary potential fields from vector observations collected within a spatially bounded region at varying satellite altitude. Our method relies on the construction of spatiospectrally localized bases of functions that mitigate the noise amplification caused by downward continuation (from the satellite altitude to the planetary surface) while balancing the conflicting demands for spatial concentration and spectral limitation. Solving simultaneously for internal and external fields in the same setting of regional data availability reduces internal-field artifacts introduced by downward-continuing unmodeled external fields, as we show with numerical examples. The AC-GVSF are optimal linear combinations of vector spherical harmonics. Their construction is not altogether very computationally demanding when the concentration domains (the regions of spatial concentration) have circular symmetry, e.g., on spherical caps or rings - even when the spherical-harmonic bandwidth is large. Data inversion proceeds by solving for the expansion coefficients of truncated function sequences, by least-squares analysis in a reduced-dimensional space. Hence, our method brings high-resolution regional potential-field modeling from incomplete and noisy vector-valued satellite data within reach of contemporary desktop machines.Comment: Under revision for Geophys. J. Int. Supported by NASA grant NNX14AM29

Spatiospectral concentration of vector fields on a sphere

We construct spherical vector bases that are bandlimited and spatially concentrated, or, alternatively, spacelimited and spectrally concentrated, suitable for the analysis and representation of real-valued vector fields on the surface of the unit sphere, as arises in the natural and biomedical sciences, and engineering. Building on the original approach of Slepian, Landau, and Pollak we concentrate the energy of our function bases into arbitrarily shaped regions of interest on the sphere, and within certain bandlimits in the vector spherical-harmonic domain. As with the concentration problem for scalar functions on the sphere, which has been treated in detail elsewhere, a Slepian vector basis can be constructed by solving a finite-dimensional algebraic eigenvalue problem. The eigenvalue problem decouples into separate problems for the radial and tangential components. For regions with advanced symmetry such as polar caps, the spectral concentration kernel matrix is very easily calculated and block-diagonal, lending itself to efficient diagonalization. The number of spatiospectrally well-concentrated vector fields is well estimated by a Shannon number that only depends on the area of the target region and the maximal spherical-harmonic degree or bandwidth. The spherical Slepian vector basis is doubly orthogonal, both over the entire sphere and over the geographic target region. Like its scalar counterparts it should be a powerful tool in the inversion, approximation and extension of bandlimited fields on the sphere: vector fields such as gravity and magnetism in the earth and planetary sciences, or electromagnetic fields in optics, antenna theory and medical imaging.Comment: Submitted to Applied and Computational Harmonic Analysi

Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules

Markov state models (MSMs) have been successful in computing metastable states, slow relaxation timescales and associated structural changes, and stationary or kinetic experimental observables of complex molecules from large amounts of molecular dynamics simulation data. However, MSMs approximate the true dynamics by assuming a Markov chain on a clusters discretization of the state space. This approximation is difficult to make for high-dimensional biomolecular systems, and the quality and reproducibility of MSMs has therefore been limited. Here, we discard the assumption that dynamics are Markovian on the discrete clusters. Instead, we only assume that the full phase- space molecular dynamics is Markovian, and a projection of this full dynamics is observed on the discrete states, leading to the concept of Projected Markov Models (PMMs). Robust estimation methods for PMMs are not yet available, but we derive a practically feasible approximation via Hidden Markov Models (HMMs). It is shown how various molecular observables of interest that are often computed from MSMs can be computed from HMMs / PMMs. The new framework is applicable to both, simulation and single-molecule experimental data. We demonstrate its versatility by applications to educative model systems, an 1 ms Anton MD simulation of the BPTI protein, and an optical tweezer force probe trajectory of an RNA hairpin

Implications of design and data quality for the analysis of a nationwide biodiversity monitoring scheme

Biodiversity monitoring schemes are designed to infer trends in biodiversity over long time periods. The value of a biodiversity monitoring program depends largely on its data quality. High quality data allow to estimate temporal trends without bias and with high precision. Data quality largely depends on the initial design of the monitoring scheme, on properly conducted fieldwork, on various aspects of quality control mechanisms, and on the methods to analyse the data. In my thesis I show and discuss implications of design and data quality presenting five case studies using data from the Swiss Biodiversity Monitoring Scheme (BDM). The BDM is a long-term programme of the Swiss Federal Office for the Environment and was initiated in 2001 to monitor Switzerland’s biodiversity. The programme focuses on changes in species richness and surveys selected species groups in a systematic sampling grid all over the country. Defined and constant sampling methods are needed to allow for unbiased and precise estimations of biodiversity trends. In Chapter I, we analysed inter-observer variation of double-sampled vegetation plots. We could show that both systematic (directed) methodological errors and random variance of species counts were small. We concluded that BDM methods are adequate for detecting biodiversity trends. In the meantime this conclusion has been widely confirmed with recent data from quality control. Chapter II focuses on detectability of species that provides the link between a raw species count and true species richness. Variation in detectability between species or habitats may considerably bias trend estimates in biological studies. We therefore asked if capture-recapture methods were suitable to analyse differences in species detectability of butterflies and looked for underlying factors that may cause variation in detectability. Because the methods available at that time were not allowing the analysis of butterfly surveys over the whole season we had to restrict it to three mid-season surveys. We found that average detectability per count was 0.61 and was influenced by observer, transect and region. Individual species during one count were detected with a mean probability of 0.50. Since the study has been published in 2007 statistical methods have been substantially developed and nowadays enable detailed analyses of butterfly communities. In the study in Chapter III we demonstrated how data from the systematic BDM surveys could be used in combination with environmental variables. We tested different sets of variables for modelling plant species richness and produced species richness maps for Switzerland by predicting species richness for each kilometre square. We found that the final models performed similarly well. Average elevation was the best single variable for explaining plant species richness nationwide. Species richness maps typically showed belt-like patterns of highest richness at intermediate altitudes. We discussed different approaches for explaining such “mid-elevational peaks” of species richness. In the frame of the BDM vascular plants, butterflies and birds are surveyed on the same sites during the same years. These simultaneous studies may be considered as a major advantage of the BDM compared to the monitoring programs in other countries. In the final two chapters we therefore inferred patterns between the species groups. Chapter IV is based on data of the first iteration of surveys. We looked at the changes that had happened in surveyed species communities of plants, birds and butterflies within the period of 5 years. As a response to climate warming we expected species to shift their distribution towards higher altitudes. We used the “Community Temperature Index” (CTI) to test for differences in reaction to climate change. As expected, in the lowlands birds and butterflies tracked climate warming with an average uphill shift of 42 and 38m respectively, while plants showed a shift of only 8m. At higher elevations there was no significant CTI change in plants and butterflies. In general our results supported the idea that reactions to climate change in alpine landscapes were lowest and alpine landscapes could be safer places because of their highly varied surfaces. In the study in Chapter V we examined to what extent distribution patterns of butterfly species are shaped by interactions with their individual host plants or, alternatively, by environmental factors. Our findings indicated that butterfly - host plant interactions were not relevant in benign environments. In contrast, at the cold distribution limits there was a strong coincidence between butterfly and plant ranges. We argued that this could be evidence for butterfly species being limited by the distribution of their host plants in harsh environments and discussed the implications of the findings under climate change conditions. Finally I summarized the most important results and also included more recent experiences from other studies using BDM data and from unpublished analyses, e.g. from quality control. I concluded in discussing the strength and weaknesses of long-monitoring programmes and pointed out that they should be considered as a complementary data source and reference for experimentally orientated research

Modelling public risk evaluation of natural hazards: a conceptual approach

International audienceIn recent years, the dealing with natural hazards in Switzerland has shifted away from being hazard-oriented towards a risk-based approach. Decreasing societal acceptance of risk, accompanied by increasing marginal costs of protective measures and decreasing financial resources cause an optimization problem. Therefore, the new focus lies on the mitigation of the hazard's risk in accordance with economical, ecological and social considerations. This modern proceeding requires an approach in which not only technological, engineering or scientific aspects of the definition of the hazard or the computation of the risk are considered, but also the public concerns about the acceptance of these risks. These aspects of a modern risk approach enable a comprehensive assessment of the (risk) situation and, thus, sound risk management decisions. In Switzerland, however, the competent authorities suffer from a lack of decision criteria, as they don't know what risk level the public is willing to accept. Consequently, there exists a need for the authorities to know what the society thinks about risks. A formalized model that allows at least a crude simulation of the public risk evaluation could therefore be a useful tool to support effective and efficient risk mitigation measures. This paper presents a conceptual approach of such an evaluation model using perception affecting factors PAF, evaluation criteria EC and several factors without any immediate relation to the risk itself, but to the evaluating person. Finally, the decision about the acceptance Acc of a certain risk i is made by a comparison of the perceived risk Ri,perc with the acceptable risk Ri,acc