Search results

Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity.

Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria.

The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error.

Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and investment activity within the private rental sector. The primary purpose of this paper aims to analyse the local variation and spatial heterogeneity in residential rental prices in a large urban market in the UK using various geo-statistical approaches.

Applying achieved price data derived from a leading internet-based rental agency for Belfast Northern Ireland is analysed in a number of spatially based modelling frameworks encompassing more traditional approaches such as hedonic regressive models to more complex spatial filtering methods to estimate rental values as a function of the properties implicit characteristics and spatial measures.

The principal findings show the efficacy of the geographically weighted regression (GWR) technique as it provides increased accuracy in predicting marginal price estimates relative to other spatial techniques. The results reveal complex spatial non-stationarity across the Belfast metropole emphasizing the premise of location in determining and understanding rental market performance. A key finding emanating from the research is that the high level of segmentation across localised pockets of the Belfast market, as a consequence of socio-political conflict and ethno-religious territoriality segregation, requires further analytical insight and model specification in order to understand the exogenous spatial and societal effects/implications for rental value.

This study is one of only a few investigations of spatial residential rent price variation applying the GWR methodology, spatial filtering and other spatial techniques within the confines of a UK housing market. In the context of residential rent prices, the research highlights that a soft segmentation modelling approaches are essential for understanding rental gradients in a polarised ethnocratic city.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.

This study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure.

Key demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana.

The process and data used in this study provide a “proof of concept” for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.

To survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.

For the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.

Based on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

The purpose of this study is to investigate the coherent structures of pulsed opposing jets by large eddy simulation (LES) model and proper orthogonal decomposition (POD) snapshot method. Flow pulsation as an active flow control method is considered for the enhancement of transport phenomena in impinging jets. The effect of flow pulsation parameters such as pulsation signal shape and frequency on the vortical coherent structures, the energy content of primary modes and their variation are studied numerically.

In this study, flow field of turbulent pulsating opposing jets has been simulated using LES. The result of the simulation in different time steps (snapshots) are stored and POD is applied on the snapshots. In this study, the POD method and calculation of spatial modes has been done using OpenFOAM, and time coefficients have been calculated using a MATLAB code.

The results of this study show that the flow excitation has a great effect on the coherent structure formation and the energy containment of fundamental modes of the flow. When the flow was excited by a harmonic sinusoidal or step function, the turbulent kinetic energy accumulated in the set of primary modes. On the other hand, the pulsed opposing jets had more regularity compared to the steady jets. The shapes, patterns and energy values of dominant modes depended on the inlet pulsation signal. An increase in pulsation frequency leads to an augmentation in energy content of the primary modes.

The predictions may be extended to include various pulsation conditions such as: various amplitudes, Reynolds number and aspect ratio.

The results of this study are a valuable source of information for active control of transport phenomena in opposing jet configurations which is used in different industrial applications such as cooling, combustion, reactors, heating and drying processes.

In this study, the coherent structures and energy content of primary modes was studied for the first time by LES model and POD snapshot method and a comprehensive discussion on numerical results is provided.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements.

First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency.

The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist.

The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.