Spatial Data

Konu hk. Yağmur Yıldırım'ın yaptığı bir program:
https://www.youtube.com/watch?v=e9Z_xN3FeE8
[Şehir Hepimizin programının 216. bölümünde, sosyal bilimci ve öğretim üyesi konuğumuz Aslı Odman, “İşçi sağlığı yoksa, halk sağlığı da yok” diye konuştu. Odman, emek alanındaki salgın politikalarının, salgın öncesi sosyal politikaların bir uzantısı olduğunu belirtti. ]

TÜRK TABİPLERİ BİRLİĞİ COVID-19 İzleme Kurulu COVID-19 PANDEMİSİ ALTINCI AY DEĞERLENDİRME RAPORU“:
COVID-19 Pandemisi İnsani, Tıbbi, Ekolojik, Ekonomik,
Sosyal ve Siyasal Bir Sorundur”

The aim of the study was to verify whether adult patients with occipital lobe epilepsy (OLE) are at risk for cognitive impairment compared with controls. Twenty patients with OLE and 20 controls, matched as closely as possible to the epilepsy group in terms of gender, age, education, and intelligence, were administered a battery of tests based on visuoperceptive domains. None of the participants was affected by progressive pathologies, received any medication other than antiepileptic drugs, nor had a psychiatric background. We noted a statistically significant difference between patients with OLE and controls in the following tasks: Perceptive Differences Test, and Object Denomination Test, and Famous Faces Test (P < 0.05). No difference was noted between symptomatic and nonsymptomatic patients with respect to neuropsychological results (P > 0.05). The linear regression analysis performed did not show any statistically significant contribution by clinical variables. Our data confirm that patients with OLE manifest subtle difficulties in processing and mental manipulation of visual spatial data.

Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression based Parameter-elevation Regressions on Independent Slopes Model (PRISM) uses spatial data sets, a knowledge base and expert interaction to generate GIS-compatible grids of climate variables. This study applied PRISM to generate maps of mean monthly and annual precipitation and minimum and maximum temperature for the Caribbean islands of Puerto Rico, Vieques and Culebra over the 1963-1995 averaging period. PRISM was run under alternative parameterizations that simulated simpler interpolation methods as well as the full PRISM model. For temperature, the standard PRISM parameterization was compared to a hypsometric method, in which the temperature/elevation slope was assumed to be -6.5°C/km (HYPS). For precipitation, the standard PRISM parameterization was compared to an inverse-distance weighting interpolation (IDW). Spatial temperature patterns were linked closely to elevation, topographic position, and coastal proximity. Both PRISM and HYPS performed well for July maximum temperature, but HYPS performed relatively poorly for January minimum temperature, due primarily to lack of a spatially varying temperature/elevation slope, vertical atmospheric layer definition, and coastal proximity guidance. Mean monthly precipitation varied significantly throughout the year, reflecting seasonally differing moisture trajectories. Spatial precipitation patterns were associated most strongly with elevation, upslope exposure to predominant moisture-bearing winds, and proximity to the ocean. IDW performed poorly compared to PRISM, due largely to the lack of elevation and moisture availability information. Overall, the full PRISM approach resulted in greatly improved performance over simpler methods for precipitation and January minimum temperature, but only a small improvement for July maximum temperature. Comparisons of PRISM mean annual temperature and precipitation maps to previously-published, hand-drawn maps showed similar overall patterns and magnitudes, but the PRISM maps provided much more spatial detail.

The effect of various geometrical parameters of a grooved staggered herringbone micromixer on the mixing performance has been investigated using computational fluid dynamics. Mixing quality has been quantified with spatial data statistics, maximum striation thickness and residence time analyses. The results show that the number of grooves per mixing cycle does not affect the mixing quality in an important way. On the other hand, a larger groove depth and width allow the maximum striation thickness to be rapidly reduced, without increasing the pressure drop across the mixer. Wide grooves, however, create significant dead zones in the microchannel, whereas deep grooves improve the spatial mixing quality.

This paper outlines the development of a method for using Kernel Estimation cluster analysis techniques to automatically identify road traffic accident &#39;black spots&#39; and &#39;black areas&#39;. Christchurch, New Zealand, was selected as the study area and data from the LTNZ crash database used to trial the technique. A GIS and Python scripting was used to implement the solution, combining spatial data for average traffic flows with the recorded accident locations. Kernel Estimation was able to quickly identify the accident clusters, and when used in conjunction with Monte Carlo simulation techniques, was able to identify statistically significant clusters.

The field of Spatial Data Infrastructure (SDI) is developing and approaches rapidly a "critical mass" of more or less operational SDIs. The purpose of the paper is to anticipate the possible impact of the maturing SDI field on its research agenda. Initial initiatives were predominantly techno centred whereas present initiatives are generally placed within current practices and developments in the exchange of spatial data between multitudes of diverse societal stakeholders. But the nature of the SDI concept is changing as well. Recent trends suggest the inclusion of data collection when needed (geo-sensors) without necessarily storing the ad-hoc collected data after use. Moreover, SDI may gradually loose its distinctiveness and become part of general information infrastructures (e.g. e-government). Once operational, SDIs need assured (self) sustainability and continuous development. Different challenges and dilemmas will also be met. The many possible data sources bring the need to choose; the initial scarcity of available data is traded for the scarcity in the ability to select. Similarly, the ability to communicate with more-and-more people (network externalities) brings diverse and often mutually conflicting worldviews and perceptions of reality. The paper first explores this maturing SDI concept as socio-technical and multi-facetted assembly, and the approaches for assessment and support of concrete SDI initiatives respectively. Future research on SDI initiatives has to address a variety of dilemmas like long-term versus short-term influences, generalization versus the ad-hoc and particular, standardization versus multiple perspectives of reality. Additionally, as SDIs become more mature and institutionalized an increase in demand for research and application of SDI monitoring and evaluation methods can be expected. It seems that contemporary GIScience is predominantly techno centred finding it difficult to accommodate the apparently reflective and socio-technical research that is needed to effectively contribute to SDI development. The paper argues that socio-technical research in this field has to deal with the dilemmas that come with developing and maturing SDI. And will employ a variety of methods as, for example, ethnography, surveys, experiments, life histories, comparative research, and historical analysis. Genuinely socio-technical SDI research is transdisciplinary and draws the realities and rationalities of the concrete SDI case into the research and therefore broadens the scope of contemporary, disciplinary SDI research. Finally, the paper suggests some challenges and consequences this might bring for contemporary GIScience praxis; specifically for its organization and researchers.