Mass real estate valuation is a multidimensional and complex matter because it depends on many constant and time-varying factors. It is desirable to have high level of model performance in the development of mass real...
详细信息
Mass real estate valuation is a multidimensional and complex matter because it depends on many constant and time-varying factors. It is desirable to have high level of model performance in the development of mass real estate valuation models for the development of sustainable real estate management strategies. For this reason, this study aims to develop a comprehensive methodology that increases the performance of mass real estate valuation models by using optimized datasets and clustering geographical value in Geographic Information Systems (GIS) modeling environment. A case study was carried out in Istanbul and Kocaeli provinces covering neighborhoods with different levels of socio-economic-development. This study was carried out using the big data, which was prepared for 121 criteria incorporating approximately 200.000 real estate values. Firstly, datasets were optimized by using the Boxplot technique concerning dataset-based outliers and Cluster and Outlier Analysis techniques were used regarding the location-based outliers. Next, 22 of the criteria affecting the value was determined with Pearson Correlation technique through analyzing the local relationship between real estate value and the criteria. Based on the result of the spatially constrained multivariate clustering Analysis (SCMCA) analysis, five different geographical value clusters with similar socio-development characteristics were detected. Mass valuation performances were tested covering all study area and these five clustered areas assessed with the use of Multiple Regression Analysis (MRA) model were used commonly in developing mass real estate valuation models. The model accuracies were evaluated through performance measurement metrics used in machine learning (MAE, MSE, RMSE) and mass real estate valuation (WtR, COD, PRD) technique that was recommended by IAAO. Considering the performances of the models, value prediction models based on geographical value clusters were more successful than the e
Temperature accelerates the deterioration processes affecting grotto temples. As such, studies of the temperature distribution characteristics of grotto temples can provide an important basis for their protection. In ...
详细信息
Temperature accelerates the deterioration processes affecting grotto temples. As such, studies of the temperature distribution characteristics of grotto temples can provide an important basis for their protection. In this paper, the hourly surface temperatures of 123 grotto temples in China were studied using ERA5-Land hourly data from 1981 to 2020, obtained through the AI Earth platform. Using the local Python development environment, the daily surface temperature difference and highest and lowest temperatures of grotto temples were linearly fitted for each year, after which the monthly average temperature difference distribution was statistically analyzed to determine trends in temperature change. Then, the GIS spatially constrained multivariate clustering method was used to cluster the surface temperature characteristics. The results showed that the grotto temples in China can be mainly divided into seven regions, namely Xinjiang, Qinghai-Xizang Plateau, Hexi, Longdong, Shaanxi and North China, Southwest, and East and Southeast. The highest average surface temperature, greater than 15 degrees C, occurred in South China, and the lowest, close to 0 degrees C, occurred in the Qinghai-Xizang Plateau. The average surface temperature of the seven regions identified showed an increasing trend. The Qinghai-Xizang Plateau was affected by severe temperature differences throughout the year, with annual average daily temperature differences approaching 30 degrees C, followed by Xinjiang and Hexi region, with a perennial temperature difference of approximately 25 degrees C. The Longdong, Shaanxi, and North China regions had annual average daily temperature differences of 15-20 degrees C, whereas values for the South China region were less than 15 degrees C. The daily surface temperature differences of grotto temples reached their maximum values in April to May and their minimum values in December to January. All studied regions are subject to temperature-induced challenges: X
Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial con-str...
详细信息
Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial con-straints regionalization and multi-objective machine learning to predict future industrial carbon emission effi-ciency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.
Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize s...
详细信息
Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize spontaneous truck platooning with coexistence of non-platooning vehicles in a network, by explicitly accounting for the interlocking relationship among platoon formation time, truck fuel saving, and increase in effective road capacity. To equilibrate the relationships, an algorithm is proposed which involves a diagonalization approach and a bush based algorithm to solve decomposed subproblems. The condition of proportionality is imposed to obtain unique traffic flows for each class of vehicles on road links. In addition, a spatially constrained multivariate clustering technique is employed to construct origin/destination zones that are smaller than the coarse Freight Analysis Framework (FAF) zones, while maintaining reasonable computational burden for network traffic assignment. Model implementation in the U.S. shows that platooning could lead to 7.9% fuel saving among platoonable trucks in 2025 and a comparable increase in effective capacity of platoonable road links, which would account for 60% of rural interstate roads. The fuel saving and road capacity improvement translate into an annual cost reduction of $868 million for the U.S. intercity trucking sector and reduced road infrastructure investment needs worth $4.8 billion. Extensive sensitivity analysis further reveals that fuel saving of platoonable trucks increases with platoon size but decreases with inter-truck distance in a platoon. Fuel saving potential suggests that priority should be given to rural rather than urban roads in deploying platooning technologies. As expected, greater market penetration of platooning technologies means higher fuel saving and greater increase in effective road capacity.
Urban competitiveness is commonly attributed to necessitating particular types of socio-spatial frameworks such as clustering on the basis of models of Marshall-Arrow-Romer (MAR), Porter, or Jacobs, since proximity is...
详细信息
Urban competitiveness is commonly attributed to necessitating particular types of socio-spatial frameworks such as clustering on the basis of models of Marshall-Arrow-Romer (MAR), Porter, or Jacobs, since proximity is known as a key perquisite for knowledge spillover, innovation, and hence urban competitiveness. Nevertheless, it is unjustifiable to unconditionally endorse the 'clustering' policy. Consequently, this research aims to examine the spatiality and geography of competition and innovation contingent upon varying spatial prerequisites and contextual circumstances. In this regard, two questions have been raised: under what circumstances urban competitiveness depends on spatial agglomeration, and whether this agglomeration follows a clustering development pattern. Through the implementation of a causal research design utilizing both in-depth interviews and social survey methodology, this study has determined the innovative characteristics and techniques for knowledge acquisition, as well as spatial behaviors and preferences of competitive industries located within Tehran. The spatially constrained multivariate clustering method was employed to group competitive industries according to their distinct substantive dissimilarities, socio-spatial behaviors, and characteristics. Then, on the basis of the structural equation modeling, the most appropriate spatial development frameworks of competitiveness were extracted with regard to the specific conditions of Tehran. Findings show that innovation, and hence competitiveness, are not always the result of clustering;clusters, as a spatial policy-making tool, have been found to be highly conducive to collaborative and intensive knowledge production, as they provide access to spillover knowledge, thereby increasing competitiveness. Despite complex economic dependencies and collaborations, some competitive industries have greater mobility and are footloose due to their Schumpeterian linear knowledge source and virtual nat
暂无评论