Marginal lands or marginal people? Analysing key processes determining the outcomes of large-scale land acquisitions in Lao PDR and Cambodia

This chapter aims to overcome the gap existing between case study research, which typically provides qualitative and process-based insights, and national or global inventories that typically offer spatially explicit and quantitative analysis of broader patterns, and thus to present adequate evidence for policymaking regarding large-scale land acquisitions. Therefore, the chapter links spatial patterns of land acquisitions to underlying implementation processes of land allocation. Methodologically linking the described patterns and processes proved difficult, but we have identified indicators that could be added to inventories and monitoring systems to make linkage possible. Combining complementary approaches in this way may help to determine where policy space exists for more sustainable governance of land acquisitions, both geographically and with regard to processes of agrarian transitions. Our spatial analysis revealed two general patterns: (i) relatively large forestry-related acquisitions that target forested landscapes and often interfere with semi-subsistence farming systems; and (ii) smaller agriculture-related acquisitions that often target existing cropland and also interfere with semi-subsistence systems. Furthermore, our meta-analysis of land acquisition implementation processes shows that authoritarian, top-down processes dominate. Initially, the demands of powerful regional and domestic investors tend to override socio-ecological variables, local actors’ interests, and land governance mechanisms. As available land grows scarce, however, and local actors gain experience dealing with land acquisitions, it appears that land investments begin to fail or give way to more inclusive, bottom-up investment models.

Eine sozialtopographische Analyse der Wohngebiete von Nuzi

The archaeological evidence from Late Bronze Age Nuzi has ever since the publication of R.F.S. Starr’s final report in 1939 experienced few attention, leaving the interpretation of the inner structure of this extraordinarily extensively excavated settlement to a thriving philological research. This paper presents a macroscopic spatial analysis of mobile inventories in the domestic areas. Based on the comparison with stationary installations and the formal architectural structure a revised socio-topography is proposed. The combination with the evidence from the investigations of the private archives elucidates the great potential for the consideration of multiple approaches in the future research on the function, meaning and sociology of spaces in Near Eastern Archaeology.

Geomorphology and spectrophotometry of Philae's landing site on comet 67P/Churyumov-Gerasimenko

Context. On 12 November 2014 the European mission Rosetta succeeded in delivering a lander, named Philae, on the surface of one of the smallest, low-gravity and most primitive bodies of the solar system, the comet 67P/Churyumov-Gerasimenko (67P). Aims. The aim of this paper is to provide a comprehensive geomorphological and spectrophotometric analysis of Philae's landing site (Agilkia) to give an essential framework for the interpretation of its in situ measurements. Methods. OSIRIS images, coupled with gravitational slopes derived from the 3D shape model based on stereo-photogrammetry were used to interpret the geomorphology of the site. We adopted the Hapke model, using previously derived parameters, to photometrically correct the images in orange filter (649.2 nm). The best approximation to the Hapke model, given by the Akimov parameter-less function, was used to correct the reflectance for the effects of viewing and illumination conditions in the other filters. Spectral analyses on coregistered color cubes were used to retrieve spectrophotometric properties. Results. The landing site shows an average normal albedo of 6.7% in the orange filter with variations of similar to 15% and a global featureless spectrum with an average red spectral slope of 15.2%/100 nm between 480.7 nm (blue filter) and 882.1 nm (near-IR filter). The spatial analysis shows a well-established correlation between the geomorphological units and the photometric characteristics of the surface. In particular, smooth deposits have the highest reflectance a bluer spectrum than the outcropping material across the area. Conclusions. The featureless spectrum and the redness of the material are compatible with the results by other instruments that have suggested an organic composition. The observed small spectral variegation could be due to grain size effects. However, the combination of photometric and spectral variegation suggests that a compositional differentiation is more likely. This might be tentatively interpreted as the effect of the efficient dust-transport processes acting on 67P. High-activity regions might be the original sources for smooth fine-grained materials that then covered Agilkia as a consequence of airfall of residual material. More observations performed by OSIRIS as the comet approaches the Sun would help interpreting the processes that work at shaping the landing site and the overall nucleus.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.

Statistical analysis of multichannel scalp field data

High density spatial and temporal sampling of EEG data enhances the quality of results of electrophysiological experiments. Because EEG sources typically produce widespread electric fields (see Chapter 3) and operate at frequencies well below the sampling rate, increasing the number of electrodes and time samples will not necessarily increase the number of observed processes, but mainly increase the accuracy of the representation of these processes. This is namely the case when inverse solutions are computed. As a consequence, increasing the sampling in space and time increases the redundancy of the data (in space, because electrodes are correlated due to volume conduction, and time, because neighboring time points are correlated), while the degrees of freedom of the data change only little. This has to be taken into account when statistical inferences are to be made from the data. However, in many ERP studies, the intrinsic correlation structure of the data has been disregarded. Often, some electrodes or groups of electrodes are a priori selected as the analysis entity and considered as repeated (within subject) measures that are analyzed using standard univariate statistics. The increased spatial resolution obtained with more electrodes is thus poorly represented by the resulting statistics. In addition, the assumptions made (e.g. in terms of what constitutes a repeated measure) are not supported by what we know about the properties of EEG data. From the point of view of physics (see Chapter 3), the natural “atomic” analysis entity of EEG and ERP data is the scalp electric field

Contextualizing local-scale point sample data using global-scale spatial datasets: Lessons learnt from the analysis of large-scale land acquisitions

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target. First, we analysed land deal datasets of varying geospatial accuracy and varying sizes and compared the results in terms of land cover, population density, and two indicators for agricultural potential: yield gap and availability of uncultivated land that is suitable for rainfed agriculture. We found that an increase in geospatial accuracy led to a substantial and greater change in conclusions about the land cover types targeted than an increase in sample size, suggesting that using a sample of higher geospatial accuracy does more to improve results than using a larger sample. The same finding emerged for population density, yield gap, and the availability of uncultivated land suitable for rainfed agriculture. Furthermore, the statistical median proved to be more consistent than the mean when comparing the descriptive statistics for datasets of different geospatial accuracy. Second, we analysed effects of geospatial accuracy on estimations regarding the potential for advancing agricultural development in target contexts. Our results show that the target contexts of the majority of land deals in our sample whose geolocation is known with a high level of accuracy contain smaller amounts of suitable, but uncultivated land than regional- and national-scale averages suggest. Consequently, the more target contexts vary within a country, the more detailed the spatial scale of analysis has to be in order to draw meaningful conclusions about the phenomena under investigation. We therefore advise against using national-scale statistics to approximate or characterize phenomena that have a local-scale impact, particularly if key indicators vary widely within a country.