scholarly journals Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 143 ◽  
Author(s):  
Daikun Wang ◽  
Victor Jing Li ◽  
Huayi Yu
Keyword(s):  
Real Estate ◽  
Large Data ◽  
Ordinary Least Squares ◽  
Core Area ◽  
Weighted Regression ◽  
Data Sets ◽  
Residential Areas ◽  
Urban Centers ◽  
Policy Makers ◽  
Evaluation Approach

The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R2 of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics of price-related parameters in high-density residential areas, providing an efficient evaluation approach for planning, land management, taxation, insurance, finance and other related fields.

This Bridge Feels Like a Tightrope: For Critical Scholars Who Engage in Policy Research

2019 ◽  
Vol 63 (3) ◽  
pp. 404-420 ◽  
Author(s):  
Sosanya Jones
Keyword(s):  
Educational Policy ◽  
Policy Research ◽  
Large Data ◽  
Research Process ◽  
Large Data Sets ◽  
Data Sets ◽  
Academic Journal ◽  
Policy Makers ◽  
Academic Researchers ◽  
Widespread Belief

There is a widespread belief among academic researchers and academic journal editors that policy audiences prefer positivist research, despite evidence that policy makers would prefer to see researchers engage more with case studies, historical analyses, and compelling voices. This belief inevitably shapes the culture of the educational policy research community and bleeds into the academic socialization of novice educational policy researchers. In this article, I use autoethnography to explore the methodological tensions I encountered as an untrained critical researcher participating in a postpositivist multicase research study that privileged large data sets, quantified qualitative findings, and entitled majority viewpoints. Through this exploration, I seek to advocate for deeper reflexivity and transparency among qualitative researchers who encounter moments of conflict and doubt in the research process. A list of recommendations for novice and seasoned educational policy researchers are provided.

From the Critical Zone to decision support tools for China's agriculture

2020 ◽  
Author(s):  
Paul Hallett ◽  
Keyword(s):  
Decision Support ◽  
Water Resources ◽  
Human Impacts ◽  
Water Security ◽  
Large Data ◽  
Critical Zone ◽  
Data Sets ◽  
Decision Support Tools ◽  
Policy Makers ◽  
Support Tools

<p>A number of critical zone observatories across China have focussed on human impacts caused by agriculture, particularly the sustainability of soil and water resources.   Using the CZO approach of measuring from the top of vegetation, through soil, to the bedrock below, joint China/UK projects at these CZOs have quantified large pools of previously undocumented nitrogen stored at depth, pathways for water loss and pollutant transport and drivers of accentuated soil erosion.  Socioeconomic studies have found that these challenges to land and water resources tie in well with the concerns of farmers.  In two different regions of China, farmers identified fertilisers as their greatest cost and water availability as their biggest challenge.  Using large data-sets generated over the past 4 years in these projects, we are developing Decision Support Tools (DSTs) underpinned by CZO science that can guide farmers and policy makers.  The work addresses food and water security in the context of climate change and diminishing resources, with an aim to improve livelihoods and sustainable economic development.  We have been guided by a review of over 400 DSTs designed for agriculture and the environment, which have been ranked in terms of their outputs and data requirements.  A goal at the EGU will be to develop links with other CZO projects to help with our DST development.</p>

scholarly journals Information Capacity Database in the Rating Model on the Basis of Polish and Italian Real Estate Markets

2016 ◽  
Vol 24 (3) ◽  
pp. 40-51
Author(s):  
Małgorzata Renigier-Biłozor ◽  
Andrzej Biłozor
Keyword(s):  
Real Estate ◽  
Large Data ◽  
Real Estate Market ◽  
Data Sets ◽  
Data Set ◽  
Real Estate Markets ◽  
The Real Estate ◽  
Redundant Data ◽  
Modified Method ◽  
Effective Use

AbstractPreliminary data analyses in decision-making systems and procedures are very important for numerous reasons, in particular because the accumulation and analysis of large data sets is costly and time-consuming. The effective use of decision support systems, including on the real estate market, requires the elimination of noise. The authors have proposed to eliminate redundant data with the use of the modified method for evaluating the capacity of the data set, which is applied in the process of classifying the condition of real estate markets. The proposed procedure (subsystem) is an attempt to improve the effectiveness of analyses relating to the development of methods for rating real estate markets. The proposed solutions will be simulated on the example of leading real estate markets in Poland and Italy.

Development of interactive diagnostic tools and metrics for the socio-economic consequences of floods

2021 ◽  
Author(s):  
Annie-Claude Parent ◽  
Frédéric Fournier ◽  
François Anctil ◽  
Brian Morse ◽  
Jean-Philippe Baril-Boyer ◽  
...  
Keyword(s):  
Maximum Flow ◽  
Large Data ◽  
Heterogeneous Data ◽  
Economic Consequences ◽  
Disaster Mitigation ◽  
Diagnostic Tools ◽  
Data Sets ◽  
Residential Areas ◽  
Level Data ◽  
The Impact

<p>Spring floods have generated colossal damages to residential areas in the Province of Quebec, Canada, in 2017 and 2019. Government authorities need accurate modelling of the impact of theoretical floods in order to prioritize pre-disaster mitigation projects to reduce vulnerability. They also need accurate modelling of forecasted floods in order to direct emergency responses. </p><p>We present a governmental-academic collaboration that aims at modelling flood impact for both theoretical and forecasted flooding events over all populated river reaches of meridional Quebec. The project, funded by the ministère de la Sécurité publique du Québec (Quebec ministry in charge of public security), consists in developing a diagnostic tool and methods to assess the risk and impacts of flooding. Tools under development are intended to be used primarily by policy makers. </p><p>The project relies on water level data based on the hydrological regimes of nearly 25,000 km of rivers, on high-precision digital terrain models, and on a detailed database of building footprints and characterizations. It also relies on 24h and 48h forecasts of maximum flow for the subject rivers. The developed tools integrate large data sets and heterogeneous data sources and produce insightful metrics on the physical extent and costs of floods and on their impact on the population. The software also provides precise information about each building affected by rising water, including an estimated cost of the damages and impact on inhabitants.  </p>

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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