A GRID-Based Spatial Interpolation Method as a Tool Supporting Real Estate Market Analyses

Szczepańska, Agnieszka; Gościewski, Dariusz; Gerus-Gościewska, Małgorzata

doi:10.3390/ijgi9010039

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…In dark blue areas, the median price of properties decreased and are likely decaying neighborhoods. It should be taken into account that the output of interpolation methods is influenced by the number of existing sample points or the problem of sample size [59,60]. Therefore, the accuracy is higher in those areas that the number of samples is high, especially in the city center, and the accuracy of interpolation methods is low in areas at the fringe of the city because they are underrepresented locations in the data [60].…”

Section: Resultsmentioning

confidence: 99%

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

Rabiei-Dastjerdi¹,

McArdle²

2021

Land

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The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted.

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Section: Resultsmentioning

confidence: 99%

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

Rabiei-Dastjerdi¹,

McArdle²

2021

Land

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“…Linear interpolators wait for the subsequent sample of the input stream x(n + 1) to compute the value of any sample at a time instant in the midst. Specifically, they compute it by adding to x(n) a term equal to t times the time derivative, which is estimated as first forward difference [32,33].…”

Section: Resampling Based On the Use Of Approximating Polynomialsmentioning

confidence: 99%

Digital Circuit for Seamless Resampling ADC Output Streams

D’Arco

Napoli

Zacharelos

2020

Sensors

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Fine resolution selection of the sample rate is not available in digital storage oscilloscopes (DSOs), so the user has to rely on offline processing to cope with such need. The paper first discusses digital signal processing based methods that allow changing the sampling rate by means of digital resampling approaches. Then, it proposes a digital circuit that, if included in the acquisition channel of a digital storage oscilloscope, between the internal analog-to-digital converter (ADC) and the acquisition memory, allows the user to select any sampling rate lower than the maximum one with fine resolution. The circuit relies both on the use of a short digital filter with dynamically generated coefficients and on a suitable memory management strategy. The output samples produced by the digital circuit are characterized by a sampling rate that can be incoherent with the clock frequency regulating the memory access. Both a field programmable gate array (FPGA) implementation and an application specific integrated circuit (ASIC) design of the proposed circuit are evaluated.

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“…Thanks to the development of measurement techniques, opportunities are currently arising to record an increasing amount of new, previously unmeasurable information about the surrounding world [1][2][3]. At the same time, both the resolution and measurement accuracy of all objects that have been subject to measurement are increasing [4,5]. Furthermore, much of the information stored in modern Spatial Information Systems (SISs) is subject to dynamic changes over a relatively short period.…”

Section: Introductionmentioning

confidence: 99%

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Gościewski,

Gerus-Gościewska,

Szczepańska

2024

Remote Sensing

Self Cite

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Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model.

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A GRID-Based Spatial Interpolation Method as a Tool Supporting Real Estate Market Analyses

Cited by 7 publications

References 45 publications

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

Digital Circuit for Seamless Resampling ADC Output Streams

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

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