Exploring the Linkage of Spatial Indicators from Remote Sensing Data with Survey Data: The Case of the Socio-Economic Panel (SOEP) and 3D City Models

This paper demonstrates the spatial evaluation of survey data from the German Socio-Economic Panel (SOEP) study using geo-coordinates and spatially relevant indicators from remote sensing data. By geocoding the addresses of survey households with block-level geographic precision (while preventing their identification by name and guaranteeingtheir complete anonymity), data on SOEP respondents can now be analyzed in a specific spatial context. In the past, regional analyses of SOEP based on official regional indicators (e.g., the unemployment rate) always had only very imprecise spatial information to work with. This limitation has now been overcome with the geocoded respondents' information. Within a protected unit of the fieldwork organization responsible for SOEP (TNS Infratest, Munich), the addresses of survey households can now be used to generate a variable describing the location of the household with block-level precision. At DIW Berlin, this additional variable is fed into a special computer infrastructure with multiple security layers that makes the socio-economic analysis possible. This paper demonstrates the use of this geographicallocation and remote sensing data to check respondents' subjective assessments of the location of their residence, anddiscusses the analytical potential of linking remote sensing data and survey data.Remote sensing data, social sciences, behavioral sciences, multi-disciplinarity, SOEP

Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, for instance to accommodate unordered categorical data. We introduce an elastic net penalty class that applies to both model forms. Additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class

Large Housing Estates – Analysing the Morphologic Similarities and Differences of a Specific Town Planning Concept

Urban Landscapes show different urban structures. The physical face of cities is the result of complex city planning and general principles of spatial planning. And this physical face can be seen as the theater of life influencing life quality, social justice, mobility patterns, etc. In this work we focus on a specific phenomenon in post-war Germany: the town planning concept of large housing estates and their physical realizations. Same principles seem to lead to very similar urban structures and morphologies. However, over time different principles of spatial planning directions were applied for large housing states in the 1950/60s (the principle of the ‘structured and low dense city’) and the 1970/80s (the principle of ‘urbanity by density’) in Western Germany and for the entire time period until 1990 in the German Democratic Republic (the principle of the ‘socialistic city’). In this stuy we analyze whether large housing estates resulted in similar or different urban morphologies. And, whether different urban morphologies developed across variations of the specific town planning concept applied. To do so, we base our work on spatial data capturing the large housing estates in Level of Detail-1 (LoD-1) 3D building models and the street network. These geoinformation are derived from multi-sensoral Earth observation data as well as from Volunteered Geographic Information (VGI) (in our case from OpenStreetMap). For the measurements and analyses of the morphologies of large housing estates we develop and apply spatial features such as building density, floor space index, orientation of buildings, orientations of streets, among others. We reveal that different directions of the same town planning concepts for large housing estates generally create physical variabilities of the urban morphologies within a relatively small range. A closer look, however, reveals that variations do exist and that specific town planning principles had de facto influence on the resulting morphologies

The dynamics of poor urban areas - analyzing morphologic transformations across the globe using Earth observation data

The urban environment is in constant motion, mostly through construction but also through destruction of urban elements. While formal development is a process with long planning periods and thus the built landscape appears static, informal or spontaneous settlements seem to be subject to high dynamics in their ever unfinished urban form. However, the dynamics and morphological characteristics of physical transformation in such settlements of urban poverty have been hardly empirically studied on a global scale or temporal consistent foundation. This paper aims at filling this gap by using Earth observation data to provide a temporal analysis of builtup transformation over a period of ~7 years in 16 documented manifestations of urban poverty. This work applies visual image interpretation using very high resolution optical satellite data in combination with in-situ and Google Street View images to derive 3D city models. We measure physical spatial structures through six spatial morphologic variables - number of buildings, size, height, orientation, heterogeneity and density. Our temporal assessment reveals inter- as well intra-urban differences and we find different, yet generally high morphologic dynamic across study sites. This is expressed in manifold ways: from demolished and reconstructed areas to such where changes appeared within the given structures. Geographically, we find advanced dynamics among our sample specifically in areas of the global south. At the same time, we observe a high spatial variability of morphological transformations within the studied areas. Despite partly high morphologic dynamics, spatial patterns of building alignments, streets and open spaces remain predominantly constant

Uncertainties of Human Perception in Visual Image Interpretation in Complex Urban Environments

Today satellite images are mostly exploited automatically due to advances in image classification methods. Manual visual image interpretation (MVII), however, still plays a significant role e.g., to generate training data for machine-learning algorithms or for validation purposes. In certain urban environments, however, of e.g., highest densities and structural complexity, textural and spectral complications in overlapping roof-structures still demand the human interpreter if one aims to capture individual building structures. The cognitive perception and real-world experience are still inevitable. Against these backgrounds, this article aims at quantifying and interpreting the uncertainties of mapping rooftop footprints of such areas. We focus on the agreement among interpreters and which aspects of perception and elements of image interpretation affect mapping. Ten test persons digitized six complex built-up areas. Hereby, we receive quantitative information about spatial variables of buildings to systematically check the consistency and congruence of results. An additional questionnaire reveals qualitative information about obstacles. Generally, we find large differences among interpreters’ mapping results and a high consistency of results for the same interpreter. We measure rising deviations correlate with a rising morphologic complexity. High degrees of individuality are expressed e.g., in time consumption, insitu-or geographic information system (GIS)-precognition whereas data source mostly influences the mapping procedure. By this study, we aim to fill a gap as prior research using MVII often does not implement an uncertainty analysis or quantify mapping aberrations. We conclude that remote sensing studies should not only rely unquestioned on MVII for validation; furthermore, data and methods are needed to suspend uncertainty

Integrating Remote Sensing and Social Science - The correlation of urban morphology with socioeconomic parameters

The alignment, small-scale transitions and characteristics of buildings, streets and open spaces constitute a heterogeneous urban morphology. The urban morphology is the physical reflection of a society that created it, influenced by historical, social, cultural, economic, political, demographic and natural conditions as well as their developments. Within the complex urban environment homogeneous physical patterns and sectors of similar building types, structural alignments or similar built-up densities can be localized and classified. Accordingly, it is assumed that urban societies also feature a distinctive socioeconomic urban morphology that is strongly correlated with the characteristics of a city’s physical morphology: Social groups settle spatially with one’s peer more or less segregated from other social groups according to, amongst other things, their economic status. This study focuses on the analysis, whether the static physical urban morphology correlates with socioeconomic parameters of its inhabitants – here with the example indicators income and value of property. Therefore, the study explores on the capabilities of high resolution optical satellite data (Ikonos) to classify patterns of urban morphology based on physical parameters. In addition a household questionnaire was developed to investigate on the cities socioeconomic morphology

Detecting the Upturn of the Solar 8^8B Neutrino Spectrum with LENA

LENA (Low Energy Neutrino Astronomy) has been proposed as a next generation 50 kt liquid scintillator detector. The large target mass allows a high precision measurement of the solar $^8$B neutrino spectrum, with an unprecedented energy threshold of 2 MeV. Hence, it can probe the MSW-LMA prediction for the electron neutrino survival probability in the transition region between vacuum and matter-dominated neutrino oscillations. Based on Monte Carlo simulations of the solar neutrino and the corresponding background spectra, it was found that the predicted upturn of the solar $^8$B neutrino spectrum can be detected with 5 sigma significance after 5 y

Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP)

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.SOEP, Geographically Referenced Data, Feasibility Study, Air Pollution, EURAD-IM, Physical Health