A statistical model for estimating mean maximum urban heat island

Investigations concentrated on the urban heat island (UHI) in its strongest development during the diurnal cycle. Task includes development of statistical models in the heating and non-heating seasons using urban surface parameters (built-up and water surface ratios, sky view factor, building height) and their areal extensions. Model equations were determined by means of stepwise multiple linear regression analysis. As the results show, there is a clear connection between the spatial distribution of the UHI and the examined parameters, so these parameters play an important role in the evolution of the UHI intensity field. Among them the sky view factor and the building height are the most determining factors, which are in line with the urban surface energy balance

The 3dskyview extension: an urban geometry access tool in a geographical information system

Implemented as a tool in a 3D Geographical Information System (GIS), the 3DSKYVIEW Extension introduced here calculates sky view factors values, while generating its graphical representation on both 2D and 3D scenes. The main issue here is the determination of sky view factors from a 2D representation, delineating the areas corresponding to visible sky and buildings. Using an algorithm developed to be applied in ArcView, the 3DSKYVIEW extension uses the tri-dimensional information of urban canyons to automatically visualize and calculate sky view factors from vector GIS files

Pengaruh Citra Merek, Harga, Pelayanan, dan Store Atmosphere Terhadap Keputusan Pembelian Pada Dendy Sky View Tulungagung

Praktik bisnis yang semakin agresif memaksa setiap bidang usaha untuk berinovasi dan melakukan kampanye pemasaran dalam upaya menarik perhatian konsumen. Misalnya pada bidang distribusi makanan serta minuman, di mana banyak bisnis baru yang didirikan di awal hingga akhir pandemi, namun konsumen yang tersisa sangat sedikit dan semakin menurun hingga banyak bisnis usaha yang menutup gerainya. Diselenggarakannya penelitian ini guna mendapatkan tujuan yakni untuk mengetahui pengaruh citra merk, harga, pelayanan, serta store atmosphere terhadap keputusan pembelian yang dilangsungkan di Dendy Sky View. Penelitian menggunakan pendekatan kuantitatif dengan seluruh populasi konsumen Dendy Sky View, metode pengumpulan sampel dengan accidental sampling diperoleh sebanyak 100 sampel, dengan instrumen data kuesioner/angket. Analisis model mempergunakan regresi linier berganda dimana memanfaatkan program IBM SPSS Statistic 21. Citra merek, harga, pelayanan, dan store atmosphere seluruhnya secara serentak mempunyai dampak positif signifikan pada keputusan pembelian di Dendy Sky View. Secara parsial pelayanan, harga, dan citra merk tidak memiliki dampak pada keputusan pembelian. Sementara itu store atmosphere memiliki dampak positif serta signifikan terhadap keputusan pembelian di Dendy Sky View

A statistical approach for estimating mean maximum urban temperature excess.

Munkánkban a városi hősziget (UHI) maximális napi kifejlődését vizsgáltuk Szegeden, a beépítettségi paraméterek függvényében. A hőmérsékleti adatok valamint a beépítettségi arány, a vízfelszín-arány, az égbolt láthatósági index és az épületmagasság, valamint ezek területi kiterjesztései közötti kapcsolatot statisztikus modellezéssel határoztuk meg. A kapott modell-egyenleteket mindkét félévre (fűtési és nem-fűtési) többváltozós lineáris regresszió segítségével állapítottuk meg. Az eredményekből világosan látszik, hogy szignifikáns kapcsolat mutatható ki a maximális UHI területi eloszlása és a beépítettségi paraméterek között, ami azt jelenti, hogy e tényezők fontos szerepet jatszanak a városi hőmérsékleti többlet területi eloszlásának kialakításában. A városi paraméterek közül az égbolt láthatósági index és az épületmagasság a leginkább meghatározó tényező, ami összhangban van a városi felszín energia-egyenlegével. | Investigations concentrated on the urban heat island (UHI) in its strongest development during the diurnal cycle in Szeged, Hungary. Task includes development of statistical models in the heating and non-heating seasons using urban surface parameters (built-up and water surface ratios, sky view factor, building height) and their areal extensions. Model equations were determined by means of stepwise multiple linear regression analysis. As the results show, there is a clear connection between the spatial distribution of the UHI and the examined parameters, so these parameters play an important role in the evolution of the UHI intensity field. Among them the sky view factor and the building height are the most determining factors, which are in line with the urban surface energy balance

A 3D-gis extensionf for sky view factors assessment in urban environment

This paper presents a Geographic Information System (GIS) 3D extension that is a tool to simulate the representation of sky view factors (SVF) while calculating their values. The sky view factor (SVF) is a climatological parameter used to characterize radiation properties on urban areas and to express the relationship between the visible area of the sky and the portion of the sky covered by buildings viewed from a specific point of observation. The implementation of this tool in a 3D GIS is useful not only because it allows straight and quick urban geometry analysis from several points of observation, but also because it can help to predict sky view factors due to future buildings without the usual associated costs of cameras and image processing. The algorithm was developed by applying the software ArcView GIS1 and its 3D Analyst extension, allowing an automatic delineation of the visible sky and obstructions

Derivation of Sky-View Factors from LIDAR Data

The use of Lidar (Light Detection and Ranging), an active light-emitting instrument, is becoming increasingly common for a range of potential applications. Its ability to provide fine resolution spatial and vertical resolution elevation data makes it ideal for a wide range of studies. This paper demonstrates the capability of Lidar data to measure sky view factors (SVF). The Lidar data is used to generate a spatial map of SVFs which are then compared against photographically-derived SVF at selected point locations. At each location three near-surface elevations measurements were taken and compared with collocated Lidar-derived estimated. It was found that there was generally good agreement between the two methodologies, although with decreasing SVF the Lidar-derived technique tended to overestimate the SVF: this can be attributed in part to the spatial resolution of the Lidar sampling. Nevertheless, airborne Lidar systems can map sky view factors over a large area easily, improving the utility of such data in atmospheric and meteorological models

A 2MASS All-Sky View of the Sagittarius Dwarf Galaxy: IV. Modeling the Sagittarius Tidal Tails

M giants recovered from the Two Micron All-Sky Survey (2MASS) have recently been used to map the position and velocity distributions of tidal debris from the Sagittarius (Sgr) dwarf spheroidal galaxy entirely around the Galaxy. We compare this data set to both test particle orbits and N-body simulations of satellite destruction run within a variety of rigid Milky Way potentials and find that the mass of the Milky Way within 50 kpc of its center should be 3.8-5.6 x 10^11 Msun in order for any Sgr orbit to simultaneously fit the velocity gradient in the Sgr trailing debris and the apocenter of the Sgr leading debris. Orbital pole precession of young debris and leading debris velocities in regions corresponding to older debris provide contradictory evidence in favor of oblate/prolate Galactic halo potentials respectively, leading us to conclude that the orbit of Sgr has evolved over the past few Gyr. Based upon the velocity dispersion and width along the trailing tidal stream we estimate the current bound mass of Sgr to be M_Sgr = 2 - 5 x 10^8 Msun independant of the form of the Galactic potential; this corresponds to a range of mass to light ratios (M/L)_Sgr = 14 - 36 (M/L)_Sun for the Sgr core. Models with masses in this range best fit the apocenter of leading Sgr tidal debris when they orbit with a radial period of roughly 0.85 Gyr and have periGalactica and apoGalactica of about 15 kpc and 60 kpc respectively. These distances will scale with the assumed distance to the Sgr dwarf and the assumed depth of the Galactic potential. The density distribution of debris along the orbit in these models is consistent with the M giant observations, and debris at all orbital phases where M giants are obviously present is younger (i.e. was lost more recently from the satellite) than the typical age of a Sgr M giant star.Comment: 42 pages, 13 figures; Accepted for publication by ApJ (October 08, 2004; originally submitted May 10, 2004). Fixed typos and added references. PDF file with high resolution figures may be downloaded from http://www.astro.caltech.edu/~drlaw/Papers/Sgr_paper4.pd

Sky View Factor footprints for urban climate modeling

Urban morphology is an important multidimensional variable to consider in climate modeling and observations, because it significantly drives the local and micro-scale climatic variability in cities. Urban form can be described through urban canopy parameters (UCPs) that resolve the spatial heterogeneity of cities by specifying the 3-dimensional geometry, arrangement, and materials of urban features. The sky view factor (SVF) is a dimension-reduced UCP capturing 3-dimensional form through horizon limitation fractions. SVF has become a popular metric to parameterize urban morphology, but current approaches are difficult to scale up to global coverage. This study introduces a Big-Data approach to calculate SVFs for urban areas from Google Street View (GSV). 90-degree field-of-view GSV photos are retrieved and converted into hemispherical views through equiangular projection. The fisheyes are segmented into sky and non-sky pixels using image processing, and the SVF is calculated using an annulus method. Results are compared to SVFs retrieved from GSV images segmented using deep learning. SVF footprints are presented for urban areas around the world tallying 15,938,172 GSV locations. Two use cases are introduced: (1) an evaluation of a Google Earth Engine classified Local Climate Zone map for Singapore; (2) hourly sun duration maps for New York and San Francisco