From buildings to cities: techniques for the multi-scale analysis of urban form and function

The built environment is a significant factor in many urban processes, yet direct measures of built form are seldom used in geographical studies. Representation and analysis of urban form and function could provide new insights and improve the evidence base for research. So far progress has been slow due to limited data availability, computational demands, and a lack of methods to integrate built environment data with aggregate geographical analysis. Spatial data and computational improvements are overcoming some of these problems, but there remains a need for techniques to process and aggregate urban form data. Here we develop a Built Environment Model of urban function and dwelling type classifications for Greater London, based on detailed topographic and address-based data (sourced from Ordnance Survey MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function and residential type analysis, where both local-scale urban clustering and city-wide trends in density and agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in commercial function data and problems with temporal attribution. These limitations currently restrict the more advanced applications of the Built Environment Model

Algorithms of causal inference for the analysis of effective connectivity among brain regions

In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC*) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity

Integrating spatial indicators in the surveillance of exploited marine ecosystems

Spatial indicators are used to quantify the state of species and ecosystem status, that is the impacts of climate and anthropogenic changes, as well as to comprehend species ecology. These metrics are thus, determinant to the stakeholder's decisions on the conservation measures to be implemented. A detailed review of the literature (55 papers) showed that 18 spatial indicators were commonly used in marine ecology. Those indicators were than characterized and studied in detail, based on its application to empirical data (a time series of 35 marine species spatial distributions, sampled either with a random stratified survey or a regular transects surveys). The results suggest that the indicators can be grouped into three classes, that summarize the way the individuals occupy space: occupancy (the area occupied by a species), aggregation (spreading or concentration of species biomass) and quantity dependent (indicators correlated with biomass), whether these are spatially explicit (include the geographic coordinates, e.g. center of gravity) or not. Indicator's temporal variability was lower than between species variability and no clear effect was observed in relation to sampling design. Species were then classified accordingly to their indicators. One indicator was selected from each of the three categories of indicators, to represent the main axes of species spatial behavior and to interpret them in terms of occupancy-aggregation-quantity relationships. All species considered were then classified according to their relationships among those three axes, into species that under increasing abundancy, primarily increase occupancy or aggregation or both. We suggest to use these relationships along the three-axes as surveillance diagrams to follow the yearly evolution of species distributional patterns in the future.MSFD from Franceinfo:eu-repo/semantics/publishedVersio

Ecosystem Good and Service Co-Effects of Terrestrial Carbon Sequestration: Implications for the U.S. Geological Survey’s LandCarbon Methodology

This paper describes specific ways in which the analysis of ecosystem goods and services can be included in terrestrial carbon sequestration assessments and planning. It specifically reviews the U.S. Geological Survey’s LandCarbon assessment methodology for ecosystem services. The report assumes that the biophysical analysis of co-effects should be designed to facilitate social evaluation. Accordingly, emphasis is placed on natural science strategies and outputs that complement subsequent economic and distributional analysis.ecosystem services, carbon sequestration, land use planning