Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’

Digital social data are now practically ubiquitous, with increasingly large and interconnected databases leading researchers, politicians, and the private sector to focus on how such ‘big data’ can allow potentially unprecedented insights into our world. This paper investigates Twitter activity in the wake of Hurricane Sandy in order to demonstrate the complex relationship between the material world and its digital representations. Through documenting the various spatial patterns of Sandy-related tweeting both within the New York metropolitan region and across the United States, we make a series of broader conceptual and methodological interventions into the nascent geographic literature on big data. Rather than focus on how these massive databases are causing necessary and irreversible shifts in the ways that knowledge is produced, we instead find it more productive to ask how small subsets of big data, especially georeferenced social media information scraped from the internet, can reveal the geographies of a range of social processes and practices. Utilizing both qualitative and quantitative methods, we can uncover broad spatial patterns within this data, as well as understand how this data reflects the lived experiences of the people creating it. We also seek to fill a conceptual lacuna in studies of user-generated geographic information, which have often avoided any explicit theorizing of sociospatial relations, by employing Jessop et al.’s TPSN framework. Through these interventions, we demonstrate that any analysis of user-generated geographic information must take into account the existence of more complex spatialities than the relatively simple spatial ontology implied by latitude and longitude coordinates.     

Can field data constrain rock viscosities?

In the 1960s it looked as though the ratio of wavelength to thickness of folds along shortened competent single layers might allow constraint of the viscosity ratios between the layers and their hosts when they deformed together. In the 1970s, the possibility arose that simple field measurements of boudins and mullions might also constrain rock viscosity ratios and thereby distinguish deformation facies and map rock viscosities in pressure–temperature–time space. Even more potential tools for constraining rock viscosities appeared in the 1980s but since then progress appears to have stagnated in a welter of problems.An attempt is made to refocus attention on direct retrospective measurements of rock rheologies during natural deformations by reviewing the potential field tools for constraining rock viscosities, discussing some of their problems, and by a crude application of the most developed approach. Further advances are likely to come from iteration between modellers and structural geologists working in a variety of tectonic settings. As well as constraining the pressure–temperature–time paths of our rocks, we should also be attempting to measure their viscosities.     

Multi-agent distributed data mining approach for classifying meteorology data: case study on Iran’s synoptic weather stations

Today’s organizations, industries and research centers are geographically distributed in different sites. To achieve true knowledge of business, mining massive amounts of data is necessary. In earth-related sciences such as meteorology, the date obtained from the various types of sensors is huge because of the high-frequency rate of data acquisition process and also the geographical distribution of weather stations. Therefore, the data mining and knowledge discovery process of this big and distributed data is a challenging work. In this paper, we propose a new distributed data mining approach called multi-agent hierarchical data mining to classify meteorology data, which has been collected from different sites widely distributed around the country (Iran). Our method utilizes a modified version of REPTree algorithm, which has been optimized to work in multi-agent system. To evaluate the proposed approach, it is implemented on 20 million of meteorology data record. Experimental results show that multi-agent hierarchical data mining approach can achieve significant performance improvement over centralized and parallel methods for knowledge discovery in large amounts of data.     

Analyzing state-dependent model–data comparison in multi-regime systems

An approach to analyze regime change in spatial time series datasets is followed and extended to jointly analyze a dynamical model depicting regime shift and observational data informing the same process. We analyze changes in the joint model–data regime and covariability within each regime. The method is applied to two observational datasets of equatorial sea surface temperature (TAO/TRITON array and satellite) and compared with the predicted data by the ECCO-JPL modeling system.     

The real-time city? Big data and smart urbanism

‘Smart cities’ is a term that has gained traction in academia, business and government to describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people. This paper focuses on the former and, drawing on a number of examples, details how cities are being instrumented with digital devices and infrastructure that produce ‘big data’. Such data, smart city advocates argue enables real-time analysis of city life, new modes of urban governance, and provides the raw material for envisioning and enacting more efficient, sustainable, competitive, productive, open and transparent cities. The final section of the paper provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data, technocratic governance and city development, corporatisation of city governance and technological lock-ins, buggy, brittle and hackable cities, and the panoptic city.     

Why data for a political-industrial ecology of cities?

Despite our declared era of ‘Big Data,’ we lack information on the flows of energy, water, and materials that support modern societies. These data are essential to understand how ecologies and the labor of people in far flung places supply urban areas, as well as how these resource flows are used by whom, where, and for what purpose. Like other places, the state of California is struggling with issues of data privacy and access. Water scarcity and the state’s commitments to greenhouse gas emission (GHG) mandates raise the issue of consumption and the unequal burdens that derive from it. These mandates have unveiled the lack of comparable and verifiable data to understand crucial production-consumption dynamics. This paper illustrates how spatially-explicit big data can be harnessed to delineate an urban political-industrial ecology of resource flows. Based on research using address-level energy and water use consumption data for Los Angeles County, the analysis reveals how the region’s wealthy residents use a disproportionate share of the water and energy resources. The paper also identifies structural obstacles to increasing fees and taxes or altering property rights that would reduce this consumption and foster more equitable resource use. This study has implications for theory, method, and policy related to urban sustainability, which is unobtainable without first unraveling the political-industrial ecology of the material basis of urbanization processes.     

Sundararajan transform—an application to geophysical data analysis

An integral transform, called in this study as Sundararajan transform, has been used to estimate the parameters of vertical magnetic effect of a fault structure. It differs from the well-known Hilbert transform in the property of phase shift as it yields a phase shift of 270° unlike the Hilbert transform, which is a 90° phase shifter. Other properties of the Sundararajan transform remain almost the same as the Hilbert transform. The transform has been tested on synthetic data and a field example of Lachlan Foldbelts, New South Wales, Australia. The results of this technique agree with the one published in the literature. The noise analysis has been examined and showed that it still provides acceptable results. The application of this transform to geophysical interpretation illustrates its potentiality, and it may be widely applied in various disciplines mainly in the field of communication engineering, signal, and image processing. An interesting property of this transform is that two successive transforms of a function return it to its original form unlike the Hilbert transform, which returns it to the negative of the original form. The procedure discussed may be automated.     

A wavelet method for detecting S‐waves in seismic data

Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarisation. We present a fast algorithm to detect the so‐called S‐phase in a three‐component seismic signal. This new approach combines traditional S‐phase detection methods and the discrete wavelet transform. This revised version was published online in July 2006 with corrections to the Cover Date.     

Background deduction of the Chang’E-1 gamma-ray spectrometer data

Gamma-ray spectrometer(GRS) is used to detect the elemental abundances and distributions on the lunar surface.To derive the elemental abundances,it is vital to acquire background gamma rays except lunar gamma rays.So GRS would observe background spectra in the course of earth-moon transfer on schedule.But in fact,GRS was not switched on in the course of flying toward the moon.After the CE-1 probe finished one-year mission,GRS car-ried out a test on background data on November 21?22,2008.The authors did conduct research on the methods of background deduction using 2105 hours of usable gamma-ray spectra acquired at the 200-km orbital height by the GRS and more than 5 hours of gamma-ray spectra acquired in the GRS background test.The final research results showed that the method of deducting the background using the minimum counts in the CE-1 GRS pixels is optimal for the elements,U,K and Th.The method applies to such a case that the elemental abundances in the pixel with the minimum counting rate are 0 μg/g and the continuum background counts are constant over the Moon.Based on the method of background deduction,the full energy peak counts of U,K,and Th are calculated.     

Identification of a Holocene aquifer–lagoon system using hydrogeochemical data

The hydrogeochemical characteristics of the Cabo de Gata coastal aquifer (southeastern Spain) were studied in an attempt to explain the anomalous salinity of its groundwater. This detritic aquifer is characterised by the presence of waters with highly contrasting salinities; in some cases the salinity exceeds that of seawater. Multivariate analysis of water samples indicates two groups of water (G1 and G2). Group G1 is represented in the upper part of the aquifer, where the proportion of seawater varies between 10 and 60%, whilst G2 waters, taken from the lower part of the aquifer, contain 60−70% seawater. In addition, hydrogeochemical modelling was applied, which reveals that the waters have been subject to evaporation between 25 and 35%. There was a good agreement between the modelled results and the observed water chemistry. This evaporation would have occurred during the Holocene, in a coastal lagoon environment; the resulting brines would have infiltrated into the aquifer and, due to their greater density, sunk towards the impermeable base. The characteristics of this water enabled us to reconstruct the interactions that must have occurred between the coastal aquifer and the lagoon, and to identify the environmental conditions that prevailed in the study area during the Middle Holocene.     

Counting fish: Performative data,anglers’ knowledge-practices and environmental measurement

This paper examines how environmental resources are measured and quantified as objects of environmental science and management and how lay knowledge-producers participate in this process, alongside the state. Using a case study of recreational angling, I show how fish in English rivers and lakes are counted and anglers act as lay or amateur knowledge-producers in the state’s metrological knowledge-practices. As embodied measurement instruments, anglers create data about themselves (as ‘effort data’) and about fish (as ‘catch returns’). These data are combined with other forms of data produced by the Environment Agency in England and Wales and used for fisheries management, thus shaping water bodies and fish ecology. I show how, to support environmental measurement, the state manages not only the environment and fish, but also anglers as lay knowledge-producers, using both regulation and economic incentives; in response, anglers also use data reflexively and strategically. I therefore emphasise the heterogeneous co-productions of environmental measurement as amateur–professional, human–animal and organic–technological, and show how measuring and managing water ecologies also involves measuring and managing humans.     

Possible contribution of lower mantle magnesiowüstite metallization into geomagnetic data

Phase change of dielectric magnesiowüstite in the lower mantle may leave signatures in geomagnetic records of the globally distributed array of observatories. We investigate theoretically which may be the contribution of magnesiowüstite metallization to geomagnetic data and how the variations of magnetic susceptibility associated with this phase change may influence the Earth's field. The modeling is performed using spherical harmonic analysis (SHA) of mantle electromagnetic (EM) responses in observatory geomagnetic data at periods of decades, 11 years, 1 year, and 27 days. The existence of a lower mantle conductor is checked against monthly means of real observatory records from 1920 through 2009 obtained by preliminary processing.     

Pyrope–knorringite garnets: overview of experimental data and natural parageneses

This paper gives an analytical overview of the experimental data obtained by different authors at high P and T in the model system MgO–Al₂O₃–SiO₂–Cr₂O₃ (MASCr). A set of four simple polynomial equations is proposed for the temperature and pressure dependence of chromium content in garnet and spinel in the assemblage Gar + Opx + Es and Gar + Fo + Opx + Sp.From the first equation, one can estimate the minimum pressure at a given temperature which is required for the formation of peridotite garnets of uncertain paragenesis with a known knorringite content. A combination of the second and third equations helps estimate P and T from the chromium content of garnet and spinel from assemblages containing both minerals. If the spinel composition is unknown, but there is reason to assign garnet to a spinel-bearing paragenesis, the fourth equation is applicable for estimating pressure at given temperature.Originally, the proposed garnet–spinel geothermobarometer was developed for a harzburgite paragenesis. However, it is applicable to garnets with CaO/Cr₂O₃ < 0.903 (including lherzolitic ones), that is, those within the Pyr–Kn–Uv triangle of the reciprocal quaternary diagram Pyr–Cros–Uv–Kn.Using the above equations and an empirical P_CG geobarometer (Grütter et al., 2006), comparative geothermobarometric estimates were obtained for a set of garnet and garnet–spinel inclusions in diamonds and intergrowths with diamond, as well as garnet inclusions in spinel. If garnet has CaO/Cr₂O₃ = 0.35–0.40, the results are in good accord. For Cr-richest and Ca-poorest garnets, the P_CG barometer shows pressures 10–15% higher compared with our estimates.     

Optimal interpolation of buoy data into a deterministic wind–wave model

Third-generation wave models have been evolved in 1980s with the state-of-the-art physics of wave generation. Using these models, the real time wave estimation is made possible but, in general, it is found to be underpredicted. This is mainly due to the smoothened wind vectors from the atmospheric model. An accurate prediction of wind is thus necessary to improve the wave prediction further. A better way of overcoming the discrepancies in the wind is by the way of wave data assimilation. In the present study, an operationally efficient yet a versatile assimilation model, optimal interpolation (OI), has been presented. The weighting matrix, so-called gain matrix, has been formulated according to the model physics by which the wind generates waves. The efficiency of the assimilative model using real time buoy observations at the Arabian Sea has been evaluated and described in this article. The root mean square error reduction of wave height is found to be of the order of 30–50% at the validation stations.     

Advanced analyses of 57Fe Mössbauer data of alumino-silicate glasses

⁵⁷Fe Mössbauer spectra of iron bearing alumino-silicate glasses are analysed by two complementary methods (SID and x-VBF) especially adapted for the analysis of disordered systems by taking into account distributions of hyperfine Mössbauer parameters. Qualitative and quantitative information about the oxidation state of iron are obtained as well as information about the distribution of local environments of iron. The possibility to separate the signal of ferric iron from that of ferrous iron allows to derive precise redox ratio in favourable cases but also to analyse more sharply the different contributions to Mössbauer spectra. Using two different glass series (feldspar composition, haplo-tonalitic composition), the characteristics of the two methods are described and employed to study the effect of composition, water incorporation and oxidation state on the glass structure. Optical absorption spectroscopy is used to support the interpretation of the Mössbauer spectra in case of the feldspar glasses.