Search results

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

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 major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.

Construction activities, particularly related to transportation, have a considerable impact on the environment and air quality. This paper aims to present a geographic information systems (GIS) and computer‐aided design (CAD)‐based approach for visualizing, communicating and analysing greenhouse gas (GHG) emissions resulting from construction activities.

A methodology using GIS is developed to graphically represent spatial aspects of construction. The approach adopted involves use of a 3D model developed in CAD environment, which was synchronized with a construction schedule stored in Excel spreadsheets. GIS environment is used to link spatial and scheduling information relevant to GHG emissions from construction activities. A baseline was created to enable effective monitoring of construction emissions.

The presented GIS model has the potential to enhance visualisation of distribution and dynamic variations of GHG emissions and could help stakeholders better analyse and understand how construction activities impact the environment.

This paper presents a novel method of graphically presenting GHG and other hazardous air emissions from construction activities using a GIS‐based approach. The paper presents the result of comparing the 3D surface representation of simulated estimated and actual construction emissions to show the impact of construction activities on the environment to support the engineering analysis and decision‐making process.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

Spatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.

The study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.

The rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.

It will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.

The current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.

The purpose of this paper is to address the case of toxic metal contamination of Sepetiba Bay caused by the Ingá Company. The paper reviews the history of the contamination and discusses the current presence of metals in the bay sediments, demonstrating that the toxic metals are clearly enriched. Sepetiba Bay is prone to significant dredging activities that make metals available in the food chain, affecting human populations, mainly fishermen communities.

The study presents the case of the Ingá Company based on international literature and data provided by previous studies.

Through the analysis and compilation of diverse data from the literature, this study demonstrates that the Ingá Company is a major source of Cd, Pb and Zn due to its calamine processing activities used to obtain high purity Zn.

This study highlights important research to complete the historical scenario of heavy metal contamination of the Sepetiba Bay by Ingá Company. The results indicate that the contaminants from the Ingá Company can indeed be traced in the sediments of Sepetiba Bay. These data have the utmost value for the environmental management of this coastal system, because such high concentrations of toxic metals in marine sediments have serious implications for the environmental quality of the bay and may negatively affect biota and human health. Therefore, this study suggests that it is now necessary to monitor this region for contamination continuously.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.