Quantitative Spatial Analysis of Micro and Macro Data: Methodological Challenges and Solutions 2 |
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| Session Organisers | Professor Henning Best (TU Kaiserslautern) Dr Tobias Rüttenauer (TU Kaiserslautern) |
| Time | Thursday 18th July, 14:00 - 15:30 |
| Room | D25 |
The session intends to bring together methodological experiences made when working with spatial data in quantitative empirical social research. On the one hand, spatial data offer the opportunity to investigate the relationship between regional characteristics on the macro level. On the other hand, spatial data can be used to enrich survey data with structural information on a certain regional level, either to control for context effects or to explicitly analyse these effects and their interplay with mechanisms on the individual level. By using GIS, addresses of survey participants can be linked with objective measures of their neighbourhood (e.g. pollution data) or proximity to institutions (e.g. of educational institutions or workplaces). Thus, these data allow investigating the relevance of infrastructure distances for social action as well as processes of spatial spillovers and diffusion.
In doing so, several methodological questions arise: What kind of regional level is adequate to what kind of question and how does the choice of administrative borders influence the derived conclusions (“MAUP”)? Can we enrich survey data by information on actual travelling times and means of transportation to account for the moving or action space of participants? What are the challenges and limitations of these approaches and how can it be done reliably?
Furthermore, innovative statistical methods are necessary to adequately analyse spatial data. Various regression models (e.g. SAR, SARAR, SLX, Durbin and others) address the spatial dependence in different ways and offer alternative approaches to identify different types of spatial spillovers or spatial interdependences, in cross-sectional and longitudinal data. Which types of models are adequate for which type of questions? Which models can be used to simultaneously analyse individual and aggregate data?
In sum, in this session we are especially interested in methodological and applied studies dealing with topics of:
1. Choice of adequate regional level and handling of borders when using administrative data
2. Connection of individual data and spatially aggregate as well as infrastructural data
3. Spatial analysis of time-series and cross-sectional data
4. Modelling spatial relationships (e.g. commuting flows, distances, traveling times, social interactions)
5. Modelling spatial interaction, spillover or diffusion processes
6. Further challenges and solutions when using georeferenced data
Keywords: spatial data, geodata, geo referencing, GIS
Dr Wahideh Achbari (University of Amsterdam) - Presenting Author
Professor Wim Bernasco (The Netherlands Institute for the Study of Crime and Law Enforcement)
It has been nearly 80 years since the seminal work of Shaw and McKay (1942) appeared in which the relation between crime, place and immigration coalesced. Today aggregate-level research on the immigration-crime relationship has proliferated dramatically, which consistently arrives at the conclusion that neighborhoods with a larger proportion of foreign-born have witnessed a reduction in crime (Kubrin et al., 2018). Prior research – their methodologically robust and diverse character notwithstanding – is almost exclusively based on American data and has mostly focused on homicide. To address these issues, in this paper we rely on unique longitudinal and geocoded data in which the location of all reported crime scenes (in 2010-2015) has been traced back to Dutch neighborhoods. This data also includes the location and structural characteristics of Asylum Reception Centers, which house newly arrived immigrants. By focusing on asylum centers – containing groups that are disproportionately young, male and underprivileged – we do not just replicate existing studies, but also contribute to temporal and spatial mechanisms, by which local crime might be affected. Our Panel Fixed Effect models, controlling for a wide set of structural neighborhood characteristics, demonstrate that neighborhood crime is not statistically significantly affected by the presence or arrival of an Asylum Reception Center. However, the Spatial Autoregressive Panel Models provide evidence of an increase in crime in the vicinity (within 6 kilometers) of an asylum center. A potential mechanism is that neighborhoods with an asylum center experience more police surveillance or an increase of informal social control, which might inadvertently shift criminal activities to adjacent neighborhoods. Therefore, we cannot necessarily interpret this positive significant effect as evidence of an increase of crime caused by asylum seekers. It may also be the case that criminals are moving their activities to neighboring areas since they see less control and better opportunities there.
Mr Dietrich Oberwittler (Max Planck Institute Freiburg) - Presenting Author
Mr Dominik Gerstner (Max Planck Institute Freiburg)
Mr Goeran Koeber (Institute of Medical Biometry and Statistics, University Freiburg)
Local neighborhoods are important social contexts for building social cohesion and trust, and an important dimension of the quality of life. Daily nuisances, disorder, as well as more serious crimes may impair social cohesion and citizens’ satisfaction in urban neighborhoods. We use a recent community survey of ca. 6.500 respondents in 140 small neighbourhoods (mean size 0.5 sqkm) in two German cities for a multilevel analysis of individual and contextual influences on cohesion and satisfaction. Two independent data sources are used to measure the spatial distribution of incivilities: A systematic social observation of disorder and geocoded police calls-for-service data. We estimate the effects of these incivilities on the micro-level, on the neighborhood level, as well as their diffusion across neighbourhood boundaries applying spatial regression. It turns out that structural disadvantage is, on the whole, a more powerful predictor of social cohesion and satisfaction, and that individual differences in resilience significantly moderate the impact of local problems.
Mr Sebastian Lang (Leibniz University Hanover) - Presenting Author
Stigma-consciousness affects various aspects such as performance, well-being, interpersonal contact and the individual perception of situations (e.g. Kleck & Strenta 1980; Inzlicht 2008; Wang et al 2012, Brown & Pinel 2003; Pinel 2002). However, there is only little research on the mechanisms causing stigma-consciousness. Previous research shows that it is insufficient to explain stigma-consciousness solely on an individual level (Gurr et al. 2018; Gurr & Lang 2018; Lang & Gross under review). This contribution extends these individual level approaches by adding the societal level.
Based on our afore-mentioned work and their theoretical foundations, the labeling approach (Tannenbaum 1953; Lemert 1951; Becker 1973) and Goffman’s work on stigmatization (1963), I derive additional hypotheses on the regional level. I expect stigma-consciousness and the effect of deviations from the employment norm to vary regionally. Moreover the regional strength of prejudices should positively affect the stigma-consciousness of the unemployed (higher values meaning stronger stigma-consciousness). This effect should be moderated by regional variability of prejudices, whereas variability is measured by an indicator for diversity proposed by Dawson (2012). These indicators of exposition of the unemployed to negative attributions and the extent of informal social control should explain regional variation in stigma consciousness.
I use data from the panel study “Labour Market and Social Security” (PASS) to empirically test my expectations. Wave 7 surveys prejudices against and stigma-consciousness of the unemployed (Gurr & Jungbauer-Gans 2013). I apply multilevel regression analysis with participants at level 1 and residential area at level 2.
Preliminary results indicate significant variance between the residential areas and in the slope of unemployment duration. Moreover, regional prejudices and the variability of prejudices have a significant effect on stigma-consciousness.
Mr Andreas Filser (University of Oldenburg) - Presenting Author
Mr Richard Preetz (University of Oldenburg)
Imbalanced numbers of men and women (i.e. sex ratios) are a macro-level demographic feature of many societies or groups. A growing body of evidence suggests that imbalanced sex ratios correlate with micro-level social consequences including relationship formation patterns and timing, divorce rates, fertility timing and rates, sexual norms as well as violence and aggression. Explanations commonly argue that sex ratios shape individuals´ dyadic power on the partner market and within relationships, shifting the attractiveness of different partner market strategies. However, theoretical reasoning remains unclear on whether behavioural consequences result from conscious strategic adjustments, or unconscious endocrinal and/or normative variations. Moreover, studies use sex ratios for a variety of administrative entities, with aggregation levels varying from counties to entire nations, often for wide age ranges such as 16 to 40 year-old adults.
In an effort to address these methodological and theoretical inconsistencies, our study is the first to analyze individuals’ perceptions of local sex ratios using a geographically widespread sample. Combining the German Family Panel (pairfam) (waves 1-7) and administrative population data, we test correlations of micro-level subjective sex ratios with macro-level local sex ratios for (1) different measurement levels (states; counties; municipalities) and (2) different age operationalisations. Additionally, we analyze transitions into relationships as an example for consequences of imbalanced sex ratios and the (competing) effects of local and perceived sex ratios. Our results suggest that none of our local sex ratio measures correlate with perceived sex ratios for any level of aggregation. Local numbers of men and women do not influence individuals’ subjective sex ratios. Moreover, we find that sex ratios correlate only with female union formation when sex ratios incorporate age heterogeneity and on the county-level only. Male union formation is uncorrelated with any local sex ratio measure. Subjective sex ratios correlate significantly with union formation for both genders.