A multilevel analytical framework for studying cultural evolution in prehistoric hunter-gatherer societies

Over the past decade, a major debate has taken place on the underpinnings of cultural changes in human societies. A growing array of evidence in behavioural and evolutionary biology has revealed that social connectivity among populations and within them affects, and is affected by, culture. Yet the interplay between prehistoric hunter-gatherer social structure and cultural transmission has typically been overlooked. Interestingly, the archaeological record contains large data sets, allowing us to track cultural changes over thousands of years: they thus offer a unique opportunity to shed light on long‐term cultural transmission processes

Infrastructures connecting people: A mechanistic model for terrestrial transportation networks

Terrestrial Transportation Infrastructures (TTIs) are shaped by both socio-political and geographical factors, hence encoding crucial information about how resources and power are distributed through a territory. Therefore, analysing the structure of pathway, railway or road networks allows us to gain a better understanding of the political and social organization of the communities that created and maintained them. Network science can provide extremely useful tools to address quantitatively this issue. Here, focussing on passengers transport, we propose a methodology to shed light on the processes and forces that moulded transportation infrastructures into their current configuration, without having to rely on any additional information besides the topology of the network and the distribution of the population. Our approach is based on a simple mechanistic model that implements a wide spectrum of decision-making mechanisms (representing different power distributions) which could have driven the growth of a TTI. Thus, by adjusting a few model parameters, it is possible to generate several synthetic transportation networks, and compare across them and against the empirical system under study. An illustrative case study (i.e. the railway system in Catalonia, a region in Spain) is also provided to showcase the application of the proposed methodology. Our preliminary results highlight the potential of our approach, thus calling for further research

Modelling terrestrial route networks to understand inter-polity interactions (southern Etruria, 950-500 BC)

Ancient regional routes were vital for interactions between settlements and deeply influenced the development of past societies and their "complexification". At the same time, since any transportation infrastructure needs some level of inter-settlement cooperation to be established, they can also be regarded as an epiphenomenon of social interactions at the regional scale. Here, we propose to analyze ancient pathway networks to understand the organization of cities and villages located in a certain territory, attempting to clarify whether such organization existed and if so, how it functioned. To address such a question, we chose a quantitative approach. Adopting network science as a general framework, by means of formal models, we try to identify how the collective effort that produced the terrestrial infrastructure was directed and organized. We selected a paradigmatic case study: Iron Age southern Etruria, a very well-studied context, with detailed archaeological information about settlement patterns and an established tradition of studies on terrestrial transportation routes, perfectly suitable for testing new techniques. The results of the modelling suggest that a balanced coordinated decision-making process was shaping the route network in Etruria, a scenario which correlates well with the picture elaborated by different scholars using a more traditional technique.Comment: 29 pages, 6 figures. This version: extends and corrects text, adds 1 explanatory figure, develops conclusion

Land-Surface Parameters for Spatial Predictive Mapping and Modeling

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks

Tracing Social-Ecologial Relationships: Hāʻena, Kaua‘i, Hawai‘i

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesHousehold preferences is a topic whose relevance can be found to dominate the applied economics, but whereas urban economies view cities as production centers, this thesis aims to give importance to the role of consumption. Provision to PoIs might give explanation to what individuals value as an important asset for improvement of their quality of life in a chosen city. As such, understanding short-term rentals and real estate prices have induced various research to seek proof of impacting factors, but analysis of mid-term rent has faced the challenge of being an overlooked category. This thesis consists of an integrated three-steps approach to analyze spatial data’s impact over the mid-term room rent, choosing Lisbon as its case study. The proposed methodology constitutes use of traditional spatial econometric models and SVR, encompassing a large set of proxies for amenities that might be recognized to hold a possible impact over rent prices. The analytical frameworks’ first step is to create a suitable HPM model that captures the data well, so significant variables can be detected and analyzed as a discrete dataset. The second step applies subsets of the dataset in the creation of SVR models, in hopes of identifying the SVs influencing price variances. Finally, SOM clusters are chosen to address whether more natural order of data division exists. Results confirm the impact of proximity to various categories of amenities, but the enrichment of models with the proposed proxies of spatial data failed to corroborate attainment of model with a higher accuracy. (Nüst et al., 2018) provides a self-assessment of the reproducibility of research, and according to the criteria given, this dissertation is evaluated as: 0, 2, 1, 2, 2 (input data, preprocessing, methods, computational environment, results)