The impact of information and communications technology on commercial real estate in the new economy

Purpose – This paper seeks to critically review the conceptual frameworks that have been developed for assessing the impact of information and communications technology (ICT) on real estate. Design/methodology/approach – The research is based on a critical review of existing literature and draws from examples of previous empirical research in the field. Findings – The paper suggests that a “socio-technical framework” is more appropriate to examine ICT impact in real estate than other “deterministic” frameworks. Therefore, ICT is an important part of the new economy, but must be seen in the context of a number of other social and economic factors. Research limitations/implications – The research is based on a qualitative assessment of existing frameworks, and by using examples from commercial real estate, assesses the extent to which a “socio-technical” framework can aid understanding of ICT impact. Practical implications – The paper is important in highlighting a number of the main issues in conceptualising ICT impact in real estate and also critically examines the emergence of a new economy in the information society within the general context of real estate. The paper also highlights research gaps in the field. Originality/value – The paper deconstructs the myths of the “death of real estate” and “productivity increase means jobs loss”, in relation to office real estate. Finally, it examines some of the ways in which ICT is impacting on real estate and suggests the most important components for a future research agenda in the field of ICT and real estate impact, and will be of value to property investors, facilities managers, developers, financiers, and others.

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society

A review of climate risk information for adaptation and development planning

Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges. Copyright © 2009 Royal Meteorological Society

Evaluation of reanalysis rainfall estimates over Ethiopia

There is a pressing need for good rainfall data for the African continent both for humanitarian and climatological purposes. Given the sparseness of ground-based observations, one source of rainfall information is Numerical Weather Prediction (NWP) model outputs. The aim of this article is to investigate the quality of two NWP products using Ethiopia as a test case. The two products evaluated are the ERA-40 and NCEP reanalysis rainfall products. Spatial, seasonal and interannual variability of rainfall have been evaluated for Kiremt (JJAS) and Belg (FMAM) seasons at a spatial scale that reflects the local variability of the rainfall climate using a method which makes optimum use of sparse gauge validation data. We found that the spatial pattern of the rainfall climatology is captured well by both models especially for the main rainy season Kiremt. However, both models tend to overestimate the mean rainfall in the northwest, west and central regions but underestimate in the south and east. The overestimation is greater for NCEP in Belg season and greater for ERA-40 in Kiremt Season. ERA-40 captures the annual cycle over most of the country better than NCEP, but strongly exaggerates the Kiremt peak in the northwest and west. The overestimation in Kiremt appears to have been reduced since the assimilation of satellite data increased around 1990. For both models the interannual variability is less well captured than the spatial and seasonal variability. Copyright © 2008 Royal Meteorological Society

A comparative method to evaluate and validate stochastic parametrizations

There is a growing interest in using stochastic parametrizations in numerical weather and climate prediction models. Previously, Palmer (2001) outlined the issues that give rise to the need for a stochastic parametrization and the forms such a parametrization could take. In this article a method is presented that uses a comparison between a standard-resolution version and a high-resolution version of the same model to gain information relevant for a stochastic parametrization in that model. A correction term that could be used in a stochastic parametrization is derived from the thermodynamic equations of both models. The origin of the components of this term is discussed. It is found that the component related to unresolved wave-wave interactions is important and can act to compensate for large parametrized tendencies. The correction term is not proportional to the parametrized tendency. Finally, it is explained how the correction term could be used to give information about the shape of the random distribution to be used in a stochastic parametrization. Copyright © 2009 Royal Meteorological Society

A singular vector perspective of 4D-Var: Filtering and interpolation

Four-dimensional variational data assimilation (4D-Var) combines the information from a time sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrievals. It is shown that the 4D-Var analysis increments can be written as a linear combination of the singular vectors of a matrix which is a function of both the observational and the forecast model systems. This formulation is used to consider the filtering and interpolating aspects of 4D-Var using idealized case-studies based on a simple model of baroclinic instability. The results of the 4D-Var case-studies exhibit the reconstruction of the state in unobserved regions as a consequence of the interpolation of observations through time. The results also exhibit the filtering of components with small spatial scales that correspond to noise, and the filtering of structures in unobserved regions. The singular vector perspective gives a very clear view of this filtering and interpolating by the 4D-Var algorithm and shows that the appropriate specification of the a priori statistics is vital to extract the largest possible amount of useful information from the observations. Copyright © 2005 Royal Meteorological Society

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

ICT implants: the invasive future of identity?

Is the human body a suitable place for a microchip? Such discussion is no longer hypothetical - in fact in reality it has not been so for some years. Restorative devices such as pacemakers and cochlear implants have become well established, yet these sophisticated devices form notably intimate links between technology and the body. More recent developments in engineering technologies have meant that the integration of silicon with biology is now reaching new levels - with devices which interact directly with the brain. As medical technologies continue to advance, their potential benefits for human enhancement will become increasingly attractive, and so we need to seriously consider where this may take us. In this paper, an attempt is made to demonstrate that, in the medical context, the foundations of more advanced implantable enhancement technologies are already notably progressed, and that they are becoming more science fact than is widely considered. A number of wider moral, ethical and legal issues stem from enhancement applications and it is difficult to foresee the social consequences, the fundamental changes on our very conception of self and the impact on our identity of adoption long term. As a result, it is necessary to acknowledge the possibilities and is timely to have debate to address the wider implications these possibilities may bring.

Investigation into the adoption intention of social CRM in Saudi banks

The growth of Web 2.0 generation will have influence on developing strong relationships with customers. Even though Web 2.0 technologies and applications have gained significant attention recently by academics and practitioners, research into its potential integration with CRM system remains a poorly investigated subject. This paper aims to investigate the adoption intention of social CRM system, focusing on Saudi banks. A conceptual model was proposed based on technology organisation and environment (TOE) framework. A qualitative approach was applied to validate the research model. The finding suggests that technology infrastructure, and competitive pressures tend to be the most influential drivers to adopt social CRM. In contrast, the limited number of IT experts, security concerns, and organisational structure were found to negatively affect social CRM adoption intention.

Global to city scale urban anthropogenic heat flux: model and variability

The large scale urban consumption of energy (LUCY) model simulates all components of anthropogenic heat flux (QF) from the global to individual city scale at 2.5 × 2.5 arc-minute resolution. This includes a database of different working patterns and public holidays, vehicle use and energy consumption in each country. The databases can be edited to include specific diurnal and seasonal vehicle and energy consumption patterns, local holidays and flows of people within a city. If better information about individual cities is available within this (open-source) database, then the accuracy of this model can only improve, to provide the community data from global-scale climate modelling or the individual city scale in the future. The results show that QF varied widely through the year, through the day, between countries and urban areas. An assessment of the heat emissions estimated revealed that they are reasonably close to those produced by a global model and a number of small-scale city models, so results from LUCY can be used with a degree of confidence. From LUCY, the global mean urban QF has a diurnal range of 0.7–3.6 W m−2, and is greater on weekdays than weekends. The heat release from building is the largest contributor (89–96%), to heat emissions globally. Differences between months are greatest in the middle of the day (up to 1 W m−2 at 1 pm). December to February, the coldest months in the Northern Hemisphere, have the highest heat emissions. July and August are at the higher end. The least QF is emitted in May. The highest individual grid cell heat fluxes in urban areas were located in New York (577), Paris (261.5), Tokyo (178), San Francisco (173.6), Vancouver (119) and London (106.7). Copyright © 2010 Royal Meteorological Society

Probabilistic physically-based cloud screening of satellite infra-red imagery for operational sea surface temperature retrieval

We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.

Movement time for motion-impaired users assisted by force-feedback: effects of movement amplitude, target width and gravity well width

This paper presents a study investigating how the performance of motion-impaired computer users in point and click tasks varies with target distance (A), target width (W), and force-feedback gravity well width (GWW). Six motion-impaired users performed point and click tasks across a range of values for A, W, and GWW. Times were observed to increase with A, and to decrease with W. Times also improved with GWW, and, with the addition of a gravity well, a greater improvement was observed for smaller targets than for bigger ones. It was found that Fitts Law gave a good description of behaviour for each value of GWW, and that gravity wells reduced the effect of task difficulty on performance. A model based on Fitts Law is proposed, which incorporates the effect of GWW on movement time. The model accounts for 88.8% of the variance in the observed data.