Implementation of a land use and spatial interaction model based on random utility choices and social accounting matrices

Random utility modelling has been established as one of the main paradigms for the implementation of land use and transport interaction (LUTI) models. Despite widespread application of such models, the respective literature provides relatively little detail on the theoretical consistency of the overall formal framework of the random utility based LUTI models. To address this gap, we present a detailed formal description of a generic land use and spatial interaction model that adheres to the random utility paradigm through the explicit distinction between utility and cost across all processes that imply behaviour of agents. The model is rooted in an extended input-output table, with the workforce and households accounts being disaggregated by socio-economic type. Similarly, the land account is broken down by domestic and non-domestic land use types. The model is developed around two processes. Firstly, the generation of demand for inputs required by established production; the estimation of the level of demand between sectors, households and land use types is supported by social accounting techniques. When appropriate the implicit production functions are assumed depended on costs of inputs, which gives rise to price-elastic demands. Secondly, the spatial assignment of demanded inputs (industrial activity, workforce, land) to locations of production; here sequences of decisions are used to distribute demand (both spatially and, when necessary, a-spatially) and to propagate costs and utilities of production and consumption that emerge from imbalances between supply and demand. The implementation of this generic model is discussed in relation to the case of the Greater South East region of the UK, including London, the South East and the East of England. We present the calibration process, data requirements, necessary assumptions and resulting implications. We discuss outputs under various land use strategies and economic scenarios, such as regulated versus competing land uses, constrained versus unconstrained densities, and high versus low economic and population growth rates. By adjusting the design constraints of the spatial planning and infrastructure supply strategies we aim to improve their sustainability.

Advancing national greenhouse gas inventories for agriculture in developing countries : improving activity data, emission factors and software technology

Peer reviewedPublisher PD

ARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologies

The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Beyond ‘the Beamer, the boat and the bach’? A content analysis-based case study of New Zealand innovative firms

In this paper we will use case studies to seek to understand the dynamic innovation processes at the level of the firm and to explain the apparent 'enigma' between New Zealand's recent innovation performance and economic growth. A text-mining tool, Leximancer, (version 4) was used to analyse the case results, based on content analysis. The case studies reveal that innovation in New Zealand firms can be best described as 'internalised', and the four key factors that affect innovation in New Zealand firms are ‘Product’, ‘Market’, ‘People’ and ‘Money’. New Zealand may be an ideal place for promoting local entrepreneurship, however, many market/technology opportunities cannot be realized in such a small and isolated economy, hence the poor economic performance