FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3181252020> ?p ?o ?g. }

• W3181252020 endingPage "128321" @default.
• W3181252020 startingPage "128321" @default.
• W3181252020 abstract "Rapid urbanization not only helps the urban economy achieve rapid growth but also imposes great pressure on resources and the environment. The green and blue infrastructure (GBI) in urban areas appears to be an important measure to enhance the economic benefits of the urban environment, but related research is still limited. In this article, we combine big data and machine learning methods to investigate the economic benefits of GBI in the city of Wuhan in Hubei Province. Specifically, the daily travel routes of citizens are analyzed at two scale levels (the housing and neighborhood scales); in our analysis, we start with the structural attributes of citizens’ own houses, move on to the city street views that citizens walk through, pass by the locational amenities where citizens spend their leisure time, and arrive at open spaces located throughout the city. The above trajectory dataset includes 30 variables, which were used as the input into ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale GWR (MGWR). The results show that MGWR (adj. R 2 = 0.524 at the housing scale, while adj. R 2 = 0.869 at the neighborhood scale) with all control variables and the GBI attribute variables have the highest goodness of fit: the closer GBI is to urban residents, the higher the economic benefits are regardless of the area; and the higher the street visible green rate is, the greater the economic benefits are. Therefore, urban areas can appropriately increase the number of well-designed small GBIs located near urban residents. Our research provides insights into how big data and machine learning can be employed in frameworks to characterize the economic benefits of GBI and can be applied in other countries and regions. • Research uses machine learning with big data containing 30 variables from various GBI. • The dataset includes urban POI data, housing purchase data, satellite remote sensing images and urban street-view data. • Differences in economic benefits and spatial features of GBI at both the housing and the neighborhood scale are revealed. • HPM is enriched by adding the appreciation rate from 2015 to 2020 as a dependent variable. • The method can be easily applied to other regions when the data is available." @default.
• W3181252020 created "2021-07-19" @default.
• W3181252020 creator A5052698526 @default.
• W3181252020 creator A5081871673 @default.
• W3181252020 date "2021-09-01" @default.
• W3181252020 modified "2023-09-26" @default.
• W3181252020 title "A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan" @default.
• W3181252020 cites W1966897718 @default.
• W3181252020 cites W1971845079 @default.
• W3181252020 cites W1976999650 @default.
• W3181252020 cites W1983134767 @default.
• W3181252020 cites W2004056710 @default.
• W3181252020 cites W2012389604 @default.
• W3181252020 cites W2035395222 @default.
• W3181252020 cites W2042400943 @default.
• W3181252020 cites W2043558768 @default.
• W3181252020 cites W2046268078 @default.
• W3181252020 cites W2051688880 @default.
• W3181252020 cites W2051834685 @default.
• W3181252020 cites W2052543285 @default.
• W3181252020 cites W2059181242 @default.
• W3181252020 cites W2062505214 @default.
• W3181252020 cites W2065419044 @default.
• W3181252020 cites W2071241413 @default.
• W3181252020 cites W2073244501 @default.
• W3181252020 cites W2076229090 @default.
• W3181252020 cites W2087133333 @default.
• W3181252020 cites W2088358965 @default.
• W3181252020 cites W2091816844 @default.
• W3181252020 cites W2111722743 @default.
• W3181252020 cites W2123732509 @default.
• W3181252020 cites W2134160380 @default.
• W3181252020 cites W2137371202 @default.
• W3181252020 cites W2143415175 @default.
• W3181252020 cites W2152088629 @default.
• W3181252020 cites W2158313020 @default.
• W3181252020 cites W2292417809 @default.
• W3181252020 cites W2346441445 @default.
• W3181252020 cites W2412024995 @default.
• W3181252020 cites W2467790945 @default.
• W3181252020 cites W2794423274 @default.
• W3181252020 cites W2889538957 @default.
• W3181252020 cites W2916943301 @default.
• W3181252020 cites W2948613401 @default.
• W3181252020 cites W2962764844 @default.
• W3181252020 cites W2968973835 @default.
• W3181252020 cites W2980896258 @default.
• W3181252020 cites W2981068011 @default.
• W3181252020 cites W2989794309 @default.
• W3181252020 cites W2991981702 @default.
• W3181252020 cites W2996937128 @default.
• W3181252020 cites W3001513439 @default.
• W3181252020 cites W3001852966 @default.
• W3181252020 cites W3008310709 @default.
• W3181252020 cites W3008321265 @default.
• W3181252020 cites W3011229556 @default.
• W3181252020 cites W3016891706 @default.
• W3181252020 cites W3019445951 @default.
• W3181252020 cites W3035113138 @default.
• W3181252020 cites W3038556993 @default.
• W3181252020 cites W3040399867 @default.
• W3181252020 cites W3044170493 @default.
• W3181252020 cites W3045435521 @default.
• W3181252020 cites W3047850516 @default.
• W3181252020 cites W3119102349 @default.
• W3181252020 cites W3120606370 @default.
• W3181252020 cites W3121087679 @default.
• W3181252020 cites W3121503710 @default.
• W3181252020 cites W3135462160 @default.
• W3181252020 cites W4231165370 @default.
• W3181252020 cites W4288101107 @default.
• W3181252020 doi "https://doi.org/10.1016/j.jclepro.2021.128321" @default.
• W3181252020 hasPublicationYear "2021" @default.
• W3181252020 type Work @default.
• W3181252020 sameAs 3181252020 @default.
• W3181252020 citedByCount "11" @default.
• W3181252020 countsByYear W31812520202021 @default.
• W3181252020 countsByYear W31812520202022 @default.
• W3181252020 countsByYear W31812520202023 @default.
• W3181252020 crossrefType "journal-article" @default.
• W3181252020 hasAuthorship W3181252020A5052698526 @default.
• W3181252020 hasAuthorship W3181252020A5081871673 @default.
• W3181252020 hasBestOaLocation W31812520201 @default.
• W3181252020 hasConcept C111919701 @default.
• W3181252020 hasConcept C127413603 @default.
• W3181252020 hasConcept C134560507 @default.
• W3181252020 hasConcept C136264566 @default.
• W3181252020 hasConcept C144133560 @default.
• W3181252020 hasConcept C145940883 @default.
• W3181252020 hasConcept C147176958 @default.
• W3181252020 hasConcept C148383697 @default.
• W3181252020 hasConcept C149782125 @default.
• W3181252020 hasConcept C162324750 @default.
• W3181252020 hasConcept C205649164 @default.
• W3181252020 hasConcept C2778368647 @default.
• W3181252020 hasConcept C2778755073 @default.
• W3181252020 hasConcept C39853841 @default.
• W3181252020 hasConcept C41008148 @default.

• next