`, etc.) that has more than one element. Defaults to 1 (for ``).
76 | getTopLevel: function($scope) {
77 | for (var i = 1; i <= 6; i++) {
78 | var $headings = this.findOrFilter($scope, 'h' + i);
79 | if ($headings.length > 1) {
80 | return i;
81 | }
82 | }
83 |
84 | return 1;
85 | },
86 |
87 | // returns the elements for the top level, and the next below it
88 | getHeadings: function($scope, topLevel) {
89 | var topSelector = 'h' + topLevel;
90 |
91 | var secondaryLevel = topLevel + 1;
92 | var secondarySelector = 'h' + secondaryLevel;
93 |
94 | return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
95 | },
96 |
97 | getNavLevel: function(el) {
98 | return parseInt(el.tagName.charAt(1), 10);
99 | },
100 |
101 | populateNav: function($topContext, topLevel, $headings) {
102 | var $context = $topContext;
103 | var $prevNav;
104 |
105 | var helpers = this;
106 | $headings.each(function(i, el) {
107 | var $newNav = helpers.generateNavItem(el);
108 | var navLevel = helpers.getNavLevel(el);
109 |
110 | // determine the proper $context
111 | if (navLevel === topLevel) {
112 | // use top level
113 | $context = $topContext;
114 | } else if ($prevNav && $context === $topContext) {
115 | // create a new level of the tree and switch to it
116 | $context = helpers.createChildNavList($prevNav);
117 | } // else use the current $context
118 |
119 | $context.append($newNav);
120 |
121 | $prevNav = $newNav;
122 | });
123 | },
124 |
125 | parseOps: function(arg) {
126 | var opts;
127 | if (arg.jquery) {
128 | opts = {
129 | $nav: arg
130 | };
131 | } else {
132 | opts = arg;
133 | }
134 | opts.$scope = opts.$scope || $(document.body);
135 | return opts;
136 | }
137 | },
138 |
139 | // accepts a jQuery object, or an options object
140 | init: function(opts) {
141 | opts = this.helpers.parseOps(opts);
142 |
143 | // ensure that the data attribute is in place for styling
144 | opts.$nav.attr('data-toggle', 'toc');
145 |
146 | var $topContext = this.helpers.createChildNavList(opts.$nav);
147 | var topLevel = this.helpers.getTopLevel(opts.$scope);
148 | var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
149 | this.helpers.populateNav($topContext, topLevel, $headings);
150 | }
151 | };
152 |
153 | $(function() {
154 | $('nav[data-toggle="toc"]').each(function(i, el) {
155 | var $nav = $(el);
156 | Toc.init($nav);
157 | });
158 | });
159 | })();
160 |
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116 | YEAR: 2020
117 | COPYRIGHT HOLDER: Michael Dorman
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/R/SVFPnt.R:
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1 | .SVFPnt = function(
2 | location,
3 | obstacles,
4 | obstacles_height_field,
5 | res_angle = 5,
6 | b = 0.01
7 | ) {
8 |
9 | # Create rays
10 | angles = seq(0, 359.9999, res_angle)
11 | sun = mapply(
12 | .sunLocation,
13 | sun_az = angles,
14 | MoreArgs = list(
15 | location = location,
16 | sun_elev = 0
17 | )
18 | )
19 | rays = mapply(shadow::ray, MoreArgs = list(from = location), to = sun)
20 | rays$makeUniqueIDs = TRUE
21 | rays = do.call(sp::rbind.SpatialLines, rays)
22 |
23 | # # Add 'surface' outline to obstacles
24 | # surface = rgeos::gBuffer(location, width = 1000)
25 | # surface = SpatialPolygonsDataFrame(
26 | # Sr = surface,
27 | # data = data.frame(height = 0),
28 | # match.ID = FALSE
29 | # )
30 | # names(surface) = obstacles_height_field
31 | # surface = rbind(obstacles[, obstacles_height_field], surface)
32 |
33 | # 'obstacles' / 'surface' outline to 'lines' *** DEPENDS ON PACKAGE 'sp' ***
34 | # surface_outline = as(surface, "SpatialLinesDataFrame")
35 | obstacles_outline = as(obstacles, "SpatialLinesDataFrame")
36 |
37 | if(
38 |
39 | # 2D mode - if point is *on* a building then 'SVF=NA'
40 | (
41 | dimensions(location) == 2 & # 2D
42 | # rgeos::gIntersects(location, obstacles) # Intersects with 'obstacles'
43 | any(unlist(sf::st_intersects(sf::st_as_sf(location), sf::st_as_sf(obstacles))))
44 | ) ||
45 |
46 | # 3D mode - if point is *inside* building then 'SVF=NA'
47 | dimensions(location) == 3 & ( # 3D
48 | # rgeos::gIntersects(location, obstacles) && # Intersects with 'obstacles'
49 | any(unlist(sf::st_intersects(sf::st_as_sf(location), sf::st_as_sf(obstacles)))) &&
50 | coordinates(location)[, 3] < obstacles[location, ]@data[, obstacles_height_field] # Location z < Obstacle h
51 | )
52 | ) svf_final = NA else {
53 |
54 | svf = rep(NA, length(rays))
55 |
56 | for(i in 1:length(rays)) {
57 |
58 | # 'Line of sight' between sun and location
59 | ray1 = rays[i, ]
60 |
61 | # if(dimensions(location) == 2) {
62 |
63 | # 2D mode - Intersections with 'obstacles' outline
64 | # inter = rgeos::gIntersection(obstacles_outline, ray1)
65 | inter = sf::st_intersection(sf::st_geometry(sf::st_as_sf(obstacles_outline)), sf::st_geometry(sf::st_as_sf(ray1)))
66 | # }
67 |
68 | # if(dimensions(location) == 3) {
69 | #
70 | # # 3D mode - Intersections with 'surface' outline
71 | # inter = rgeos::gIntersection(surface_outline, ray1)
72 | #
73 | # }
74 |
75 | # No intersections means 'SVF=1'
76 | if(length(inter) == 0L) svf[i] = 1 else {
77 |
78 | # If some of the intersections are lines then convert to points
79 | # if(is(inter, "SpatialCollections")) {
80 | # lin = inter@lineobj
81 | # inter = inter@pointobj
82 | # for(lin_i in 1:length(lin)) {
83 | # lin_pnt = lin[lin_i, ]
84 | # lin_pnt = coordinates(lin_pnt)[[1]][[1]]
85 | # lin_pnt = sp::SpatialPoints(
86 | # lin_pnt,
87 | # proj4string = CRS(proj4string(inter))
88 | # )
89 | # inter = sp::rbind.SpatialPoints(inter, lin_pnt)
90 | # }
91 | if(length(grep("GEOMETRY", sf::st_geometry_type(inter))) > 0) {
92 |
93 | lin = sf::st_cast(sf::st_collection_extract(inter, "LINESTRING"), "POINT")
94 | inter = sf::st_collection_extract(inter, "POINT")
95 |
96 | inter = as(c(st_geometry(inter), st_geometry(lin)), "Spatial")
97 | }
98 |
99 | # Set row names
100 |
101 | inter = as(as(inter, "Spatial"), "SpatialPoints")
102 | row.names(inter) = 1:length(inter)
103 |
104 | # Extract 'obstacles' / 'surface' data for each intersection
105 | # if(dimensions(location) == 2) outline = obstacles_outline
106 | # if(dimensions(location) == 3) outline = surface_outline
107 | inter =
108 | SpatialPointsDataFrame(
109 | inter,
110 | sp::over(
111 | inter,
112 | # rgeos::gBuffer(obstacles_outline, byid = TRUE, width = b),
113 | as(sf::st_buffer(sf::st_as_sf(obstacles_outline), dist = b),
114 | "Spatial"),
115 | fn = max)
116 | )
117 |
118 | # Distance between examined location and intersections
119 | # inter$dist = rgeos::gDistance(inter, location, byid = TRUE)[1, ]
120 | inter$dist = sf::st_distance(sf::st_as_sf(inter), sf::st_as_sf(location))
121 |
122 | # Maximal angle of obstruction calculation
123 |
124 | # 2D mode - height difference equal to obstacles height
125 | if(dimensions(location) == 2) {
126 | inter$height_diff = inter@data[, obstacles_height_field]
127 | }
128 |
129 | # 3D mode - location height *subtracted* from obstacles height
130 | if(dimensions(location) == 3) {
131 | inter$height_diff = max(
132 | inter@data[, obstacles_height_field] - coordinates(location)[, 3],
133 | 0 # If location z > obstacles h then 'height_diff=0'
134 | )
135 | }
136 |
137 | inter$angle = rad2deg(atan(inter$height_diff / inter$dist))
138 | # inter$svf = 1 - inter$angle / 90
139 | inter$svf = 1 - sin(deg2rad(inter$angle))^2 # Gal & Unger 2014
140 | svf[i] = min(inter$svf)
141 |
142 | }
143 |
144 | }
145 |
146 | svf_final = mean(svf)
147 |
148 | }
149 |
150 | return(svf_final)
151 |
152 | }
153 |
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/R/data.R:
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1 | #' Polygonal layer of four buildings in Rishon
2 | #'
3 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of four buildings located in Rishon-Le-Zion. The attribute \code{BLDG_HT} contains building height, in meters.
4 | #'
5 | #' @format A \code{SpatialPolygonsDataFrame} with 4 features and 2 attributes:
6 | #' \describe{
7 | #' \item{build_id}{Building ID}
8 | #' \item{BLDG_HT}{Building height, in meters}
9 | #' }
10 | #' @examples
11 | #' build
12 | #' plot(build, axes = TRUE)
13 |
14 | "build"
15 |
16 | #' Polygonal layer of three buildings in Boston
17 | #'
18 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of three buildings located in Central Boston. The attribute \code{height_m} contains building height, in meters.
19 | #'
20 | #' @format A \code{SpatialPolygonsDataFrame} with 10 features and 4 attributes:
21 | #' \describe{
22 | #' \item{objectid}{Building part ID}
23 | #' \item{build_id}{Building ID}
24 | #' \item{part_floor}{Number of floors for part}
25 | #' \item{height_m}{Building height, in meters}
26 | #' }
27 | #' @examples
28 | #' boston_build
29 | #' plot(boston_build, axes = TRUE)
30 |
31 | "boston_build"
32 |
33 | #' Polygonal layer of 376 buildings in Beer-Sheva
34 | #'
35 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of 367 buildings in the Ramot neighborhood, Beer-Sheva. The attribute \code{height_m} contains building height, in meters.
36 | #'
37 | #' @format A \code{SpatialPolygonsDataFrame} with 10 features and 4 attributes:
38 | #' \describe{
39 | #' \item{build_id}{Building ID}
40 | #' \item{floors}{Number of floors for building}
41 | #' \item{apartments}{Number of apartments}
42 | #' \item{height_m}{Building height, in meters}
43 | #' \item{elev}{Elevation above sea level of building base, in meters}
44 | #' }
45 | #' @examples
46 | #' beersheva_build
47 | #' plot(beersheva_build, axes = TRUE)
48 |
49 | "beersheva_build"
50 |
51 | #' Polygonal layer of a building block in Boston
52 | #'
53 | #' A \code{SpatialPolygons} object representing the boundaries of a building block in Central Boston.
54 | #'
55 | #' @format A \code{SpatialPolygons} with a single feature.
56 | #' @examples
57 | #' boston_block
58 | #' plot(boston_block, axes = TRUE)
59 |
60 | "boston_block"
61 |
62 | #' Polygonal layer of a park in Boston
63 | #'
64 | #' A \code{SpatialPolygons} object representing the boundaries of a park in Central Boston.
65 | #'
66 | #' @format A \code{SpatialPolygons} with a single feature.
67 | #' @examples
68 | #' boston_park
69 | #' plot(boston_park, axes = TRUE)
70 |
71 | "boston_park"
72 |
73 | #' Polygonal layer of sidewalks in Boston
74 | #'
75 | #' A \code{SpatialLinesDataFrame} object representing sidewalks in Central Boston.
76 | #'
77 | #' @format A \code{SpatialLinesDataFrame} with 78 features.
78 | #' @examples
79 | #' boston_sidewalk
80 | #' plot(boston_sidewalk, axes = TRUE)
81 |
82 | "boston_sidewalk"
83 |
84 | #' Typical Meteorological Year (TMY) solar radiation in Tel-Aviv
85 | #'
86 | #' A table with hourly solar radiation estimates for a typical meteorological year in Tel-Aviv. \itemize{
87 | #' \item{\code{time} Time, as \code{character} in the \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, e.g. \code{"2000-01-01 06:00:00"}}, referring to local time
88 | #' \item{\code{sun_az} Sun azimuth, in decimal degrees from North}
89 | #' \item{\code{sun_elev} Sun elevation, in decimal degrees}
90 | #' \item{\code{solar_normal} Direct Normal Irradiance, in Wh/m^2}
91 | #' \item{\code{solar_diffuse} Diffuse Horizontal Irradiance, in Wh/m^2}
92 | #' \item{\code{dbt} Dry-bulb temperature, in Celsius degrees}
93 | #' \item{\code{ws} Wind speed, in m/s}
94 | #' }
95 | #'
96 | #' @format A \code{data.frame} with 8760 rows and 7 columns.
97 | #'
98 | #' @references
99 | #' \url{https://energyplus.net/weather-location/europe_wmo_region_6/ISR/ISR_Tel.Aviv-Bet.Dagan.401790_MSI}
100 | #' @examples
101 | #' head(tmy)
102 |
103 | "tmy"
104 |
105 | #' Typical Meteorological Year (TMY) solar radiation in Beer-Sheva
106 | #'
107 | #' A table with hourly solar radiation estimates for a typical meteorological year in Beer-Sheva. \itemize{
108 | #' \item{\code{time} Time, as \code{character} in the \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, e.g. \code{"2000-01-01 06:00:00"}}, referring to local time
109 | #' \item{\code{sun_az} Sun azimuth, in decimal degrees from North}
110 | #' \item{\code{sun_elev} Sun elevation, in decimal degrees}
111 | #' \item{\code{solar_normal} Direct Normal Irradiance, in Wh/m^2}
112 | #' \item{\code{solar_diffuse} Diffuse Horizontal Irradiance, in Wh/m^2}
113 | #' \item{\code{dbt} Dry-bulb temperature, in Celsius degrees}
114 | #' \item{\code{ws} Wind speed, in m/s}
115 | #' }
116 | #'
117 | #' @format A \code{data.frame} with 8760 rows and 7 columns.
118 | #'
119 | #' @references
120 | #' \url{https://energyplus.net/weather-location/europe_wmo_region_6/ISR/ISR_Beer.Sheva.401900_MSI}
121 | #' @examples
122 | #' head(tmy2)
123 |
124 | "tmy2"
125 |
126 | #' DEM of Ramot neighborhood, Beer-Sheva
127 | #'
128 | #' Digital Elevation Model (DEM) of Ramot neighborhood, Beer-Sheva. Raster values represent elevation above sea level, in meters.
129 | #'
130 | #' @format A \code{RasterLayer} representing a grid of 1974 raster cells, each cell is a 30*30 meters rectangle. Data source is the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global dataset.
131 | #'
132 | #' @references
133 | #' \url{https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1}
134 | #' @examples
135 | #' beersheva_elev
136 | #' plot(beersheva_elev)
137 |
138 | "beersheva_elev"
139 |
140 |
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/R/shadowFootprint.R:
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1 | #' Shadow footprint on the ground
2 | #'
3 | #' Creates a polygonal layer of shadow footprints on the ground, taking into account:\itemize{
4 | #' \item{Obstacles outline (\code{obstacles}), given by a polygonal layer with a height attribute (\code{obstacles_height_field})}
5 | #' \item{Sun position (\code{solar_pos}), given by azimuth and elevation angles}
6 | #' }
7 | #' The calculation method was inspired by Morel Weisthal's MSc thesis at the Ben-Gurion University of the Negev.
8 | #'
9 | #' @param obstacles A \code{SpatialPolygonsDataFrame} object specifying the obstacles outline
10 | #' @param obstacles_height_field Name of attribute in \code{obstacles} with extrusion height for each feature
11 | #' @param solar_pos A \code{matrix} with one row and two columns; first column is the solar azimuth (in decimal degrees from North), second column is sun elevation (in decimal degrees)
12 | #' @param time When \code{solar_pos} is unspecified, \code{time} can be passed to automatically calculate \code{solar_pos} based on the time and the centroid of \code{obstacles}, using function \code{suntools::solarpos}. In such case \code{obstacles} must have a defined CRS (not \code{NA}). The \code{time} value must be a \code{POSIXct} or \code{POSIXlt} object
13 |
14 | #' @param b Buffer size for shadow footprints of individual segments of a given polygon; used to eliminate minor internal holes in the resulting shadow polygon.
15 | #'
16 | #' @return A \code{SpatialPolygonsDataFrame} object representing shadow footprint, plus buildings outline. Object length is the same as that of the input \code{obstacles}, with an individual footprint feature for each obstacle.
17 | #'
18 | #' @references
19 | #' Weisthal, M. (2014). Assessment of potential energy savings in Israel through climate-aware residential building design (MSc Thesis, Ben-Gurion University of the Negev).
20 | #' \url{https://www.dropbox.com/s/bztnh1fi9znmswj/Thesis_Morel_Weisthal.pdf?dl=1}
21 | #'
22 | #' @examples
23 | #' time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
24 | #' proj4string(build) = CRS("EPSG:32636")
25 | #' location_geo = matrix(c(34.7767978098526, 31.9665936050395), ncol = 2)
26 | #' solar_pos = suntools::solarpos(location_geo, time)
27 | #' footprint1 = ## Using 'solar_pos'
28 | #' shadowFootprint(
29 | #' obstacles = build,
30 | #' obstacles_height_field = "BLDG_HT",
31 | #' solar_pos = solar_pos
32 | #' )
33 | #' footprint2 = ## Using 'time'
34 | #' shadowFootprint(
35 | #' obstacles = build,
36 | #' obstacles_height_field = "BLDG_HT",
37 | #' time = time
38 | #' )
39 | #' all.equal(footprint1, footprint2)
40 | #' footprint = footprint1
41 | #' plot(footprint, col = adjustcolor("lightgrey", alpha.f = 0.5))
42 | #' plot(build, add = TRUE, col = "darkgrey")
43 | #'
44 | #' @export
45 | #' @importFrom sf st_buffer
46 | #' @importFrom sf st_as_sf
47 | #' @importFrom sf st_length
48 | #' @importFrom sf st_convex_hull
49 | #' @importFrom sf st_union
50 | #' @importFrom sf st_geometry
51 | #' @importFrom methods as
52 | #' @name shadowFootprint
53 |
54 | NULL
55 |
56 | setGeneric("shadowFootprint", function(
57 | obstacles,
58 | obstacles_height_field,
59 | ...
60 | ) {
61 | standardGeneric("shadowFootprint")
62 | })
63 |
64 | #' @rdname shadowFootprint
65 |
66 | setMethod(
67 |
68 | f = "shadowFootprint",
69 |
70 | signature = c(
71 | obstacles = "SpatialPolygonsDataFrame"
72 | ),
73 |
74 | function(
75 | obstacles,
76 | obstacles_height_field,
77 | solar_pos = solarpos2(obstacles, time),
78 | time = NULL,
79 | b = 0.01
80 | ) {
81 |
82 | # Check inputs
83 | .checkSolarPos(solar_pos, length1 = TRUE)
84 | .checkObstacles(obstacles, obstacles_height_field)
85 |
86 | # Container for footprints of individual 'obstacles' features
87 | footprint_final = list()
88 |
89 | for(i in 1:length(obstacles)) {
90 |
91 | # Calculate shift distance
92 | dist = obstacles@data[i, obstacles_height_field] / tan(deg2rad(solar_pos[1,2]))
93 |
94 | # Polygon to line segments
95 | seg = shadow::toSeg(obstacles[i, ])
96 |
97 | # Discard zero-length segments
98 | # seg = seg[rgeos::gLength(seg, byid = TRUE) > 0, ]
99 | seg = seg[unclass(sf::st_length(sf::st_as_sf(seg))) > 0, ]
100 |
101 | # Shift segments
102 | seg_shifted = shadow::shiftAz(seg, az = solar_pos[1, 1], dist = -dist)
103 |
104 | # Container for footprints of individual segments of one 'obstacles' feature
105 | footprint = list()
106 |
107 | for(j in 1:length(seg)) {
108 |
109 | f = sp::rbind.SpatialLines(seg[j, ], seg_shifted[j, ], makeUniqueIDs = TRUE)
110 | # f = rgeos::gConvexHull(f)
111 | f = as(sf::st_convex_hull(sf::st_geometry(sf::st_union(sf::st_as_sf(f)))), "Spatial")
112 | footprint[[j]] = f
113 |
114 | }
115 |
116 | # Bind footprings of individual segments of one 'obstacles' feature
117 | footprint$makeUniqueIDs = TRUE
118 | footprint = do.call(sp::rbind.SpatialPolygons, footprint)
119 | # footprint = rgeos::gUnaryUnion(footprint)
120 | # footprint = rgeos::gBuffer(footprint, width = b)
121 | # footprint_final[[i]] = rgeos::gUnion(footprint, obstacles[i, ])
122 | footprint = sf::st_union(sf::st_geometry(sf::st_as_sf(footprint)))
123 | footprint = sf::st_buffer(footprint, dist = b)
124 | footprint_final[[i]] = as(sf::st_union(sf::st_geometry(footprint)), "Spatial")
125 |
126 | }
127 |
128 | # Bind footprints of individual 'obstacles' features
129 | footprint_final$makeUniqueIDs = TRUE
130 | footprint_final = do.call(sp::rbind.SpatialPolygons, footprint_final)
131 | footprint_final = SpatialPolygonsDataFrame(footprint_final, obstacles@data, match.ID = FALSE)
132 |
133 | return(footprint_final)
134 |
135 | }
136 | )
137 |
138 |
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/man/SVF.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/SVF.R
3 | \name{SVF}
4 | \alias{SVF}
5 | \alias{SVF,SpatialPoints-method}
6 | \alias{SVF,Raster-method}
7 | \title{Sky View Factor (SVF) calculation}
8 | \usage{
9 | \S4method{SVF}{SpatialPoints}(
10 | location,
11 | obstacles,
12 | obstacles_height_field,
13 | res_angle = 5,
14 | b = 0.01,
15 | parallel = getOption("mc.cores")
16 | )
17 |
18 | \S4method{SVF}{Raster}(
19 | location,
20 | obstacles,
21 | obstacles_height_field,
22 | res_angle = 5,
23 | b = 0.01,
24 | parallel = getOption("mc.cores")
25 | )
26 | }
27 | \arguments{
28 | \item{location}{A \code{SpatialPoints*} or \code{Raster*} object, specifying the location(s) for which to calculate logical shadow values. If \code{location} is \code{SpatialPoints*}, then it can have 2 or 3 dimensions. A 2D \code{SpatialPoints*} is considered as a point(s) on the ground, i.e. 3D point(s) where \eqn{z=0}. In a 3D \code{SpatialPoints*} the 3rd dimension is assumed to be elevation above ground \eqn{z} (in CRS units). \code{Raster*} cells are considered as ground locations}
29 |
30 | \item{obstacles}{A \code{SpatialPolygonsDataFrame} object specifying the obstacles outline}
31 |
32 | \item{obstacles_height_field}{Name of attribute in \code{obstacles} with extrusion height for each feature}
33 |
34 | \item{res_angle}{Circular sampling resolution, in decimal degrees. Default is 5 degrees, i.e. 0, 5, 10... 355.}
35 |
36 | \item{b}{Buffer size when joining intersection points with building outlines, to determine intersection height}
37 |
38 | \item{parallel}{Number of parallel processes or a predefined socket cluster. With \code{parallel=1} uses ordinary, non-parallel processing. Parallel processing is done with the \code{parallel} package}
39 | }
40 | \value{
41 | A numeric value between 0 (sky completely obstructed) and 1 (sky completely visible).
42 | \itemize{
43 | \item{If input \code{location} is a \code{SpatialPoints*}, then returned object is a \code{vector} where each element representing the SVF for each point in \code{location}}
44 | \item{If input \code{location} is a \code{Raster*}, then returned object is a \code{RasterLayer} where cell values express SVF for each ground location}
45 | }
46 | }
47 | \description{
48 | Calculates the Sky View Factor (SVF) at given points or complete grid (\code{location}), taking into account obstacles outline (\code{obstacles}) given by a polygonal layer with a height attribute (\code{obstacles_height_field}).
49 | }
50 | \note{
51 | SVF calculation for each view direction follows the following equation -
52 | \deqn{1 - (sin(\beta))^2}
53 | Where \eqn{\beta} is the highest elevation angle (see equation 3 in Gal & Unger 2014).
54 | }
55 | \examples{
56 | ## Individual locations
57 | # location0 = rgeos::gCentroid(build)
58 | location0 = as(sf::st_centroid(sf::st_union(sf::st_geometry(sf::st_as_sf(build)))), "Spatial")
59 | location1 = raster::shift(location0, 0, -15)
60 | location2 = raster::shift(location0, -10, 20)
61 | locations = rbind(location1, location2)
62 | svfs = SVF(
63 | location = locations,
64 | obstacles = build,
65 | obstacles_height_field = "BLDG_HT"
66 | )
67 | plot(build)
68 | plot(locations, add = TRUE)
69 | raster::text(locations, round(svfs, 2), col = "red", pos = 3)
70 |
71 | \dontrun{
72 |
73 | ## Grid
74 | ext = as(raster::extent(build), "SpatialPolygons")
75 | r = raster::raster(ext, res = 5)
76 | proj4string(r) = proj4string(build)
77 | pnt = raster::rasterToPoints(r, spatial = TRUE)
78 | svfs = SVF(
79 | location = r,
80 | obstacles = build,
81 | obstacles_height_field = "BLDG_HT",
82 | parallel = 3
83 | )
84 | plot(svfs, col = grey(seq(0.9, 0.2, -0.01)))
85 | raster::contour(svfs, add = TRUE)
86 | plot(build, add = TRUE, border = "red")
87 |
88 | ## 3D points
89 | # ctr = rgeos::gCentroid(build)
90 | ctr = as(sf::st_centroid(sf::st_union(sf::st_geometry(sf::st_as_sf(build)))), "Spatial")
91 | heights = seq(0, 28, 1)
92 | loc3d = data.frame(
93 | x = coordinates(ctr)[, 1],
94 | y = coordinates(ctr)[, 2],
95 | z = heights
96 | )
97 | coordinates(loc3d) = ~ x + y + z
98 | proj4string(loc3d) = proj4string(build)
99 | svfs = SVF(
100 | location = loc3d,
101 | obstacles = build,
102 | obstacles_height_field = "BLDG_HT",
103 | parallel = 3
104 | )
105 | plot(heights, svfs, type = "b", xlab = "Elevation (m)", ylab = "SVF", ylim = c(0, 1))
106 | abline(v = build$BLDG_HT, col = "red")
107 |
108 | ## Example from Erell et al. 2012 (p. 19 Fig. 1.2)
109 |
110 | # Geometry
111 | # pol1 = rgeos::readWKT("POLYGON ((0 100, 1 100, 1 0, 0 0, 0 100))")
112 | pol1 = as(sf::st_as_sfc("POLYGON ((0 100, 1 100, 1 0, 0 0, 0 100))"), "Spatial")
113 | # pol2 = rgeos::readWKT("POLYGON ((2 100, 3 100, 3 0, 2 0, 2 100))")
114 | pol2 = as(sf::st_as_sfc("POLYGON ((2 100, 3 100, 3 0, 2 0, 2 100))"), "Spatial")
115 | pol = sp::rbind.SpatialPolygons(pol1, pol2, makeUniqueIDs = TRUE)
116 | pol = sp::SpatialPolygonsDataFrame(pol, data.frame(h = c(1, 1)), match.ID = FALSE)
117 | # pnt = rgeos::readWKT("POINT (1.5 50)")
118 | pnt = as(sf::st_as_sfc("POINT (1.5 50)"), "Spatial")
119 | plot(pol, col = "grey", xlim = c(0, 3), ylim = c(45, 55))
120 | plot(pnt, add = TRUE, col = "red")
121 |
122 | # Fig. 1.2 reproduction
123 | h = seq(0, 2, 0.1)
124 | svf = rep(NA, length(h))
125 | for(i in 1:length(h)) {
126 | pol$h = h[i]
127 | svf[i] = SVF(location = pnt, obstacles = pol, obstacles_height_field = "h", res_angle = 1)
128 | }
129 | plot(h, svf, type = "b", ylim = c(0, 1))
130 |
131 | # Comparison with SVF values from the book
132 | test = c(1, 0.9805806757, 0.9284766909, 0.8574929257, 0.7808688094,
133 | 0.7071067812, 0.6401843997, 0.5812381937, 0.52999894, 0.4856429312,
134 | 0.4472135955, 0.4138029443, 0.3846153846, 0.3589790793, 0.336336397,
135 | 0.316227766, 0.2982749931, 0.282166324, 0.2676438638, 0.2544932993,
136 | 0.242535625)
137 | range(test - svf)
138 |
139 | }
140 |
141 | }
142 | \references{
143 | Erell, E., Pearlmutter, D., & Williamson, T. (2012). Urban microclimate: designing the spaces between buildings. Routledge.
144 |
145 | Gal, T., & Unger, J. (2014). A new software tool for SVF calculations using building and tree-crown databases. Urban Climate, 10, 594-606.
146 | }
147 |
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/docs/reference/deg2rad.html:
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/docs/reference/rad2deg.html:
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/docs/reference/boston_block.html:
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9 | Polygonal layer of a building block in Boston — boston_block • shadow
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114 | Polygonal layer of a building block in Boston
115 | Source: R/data.R
116 | boston_block.Rd
117 |
118 |
119 |
120 | A SpatialPolygons object representing the boundaries of a building block in Central Boston.
121 |
122 |
123 | boston_block
124 |
125 |
126 | Format
127 |
128 | A SpatialPolygons with a single feature.
129 |
130 | Examples
131 | boston_block
132 | #> class : SpatialPolygons
133 | #> features : 1
134 | #> extent : 328903.9, 329144.9, 4690539, 4690799 (xmin, xmax, ymin, ymax)
135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs 
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/docs/reference/boston_park.html:
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120 | A SpatialPolygons object representing the boundaries of a park in Central Boston.
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123 | boston_park
124 |
125 |
126 | Format
127 |
128 | A SpatialPolygons with a single feature.
129 |
130 | Examples
131 | boston_park
132 | #> class : SpatialPolygonsDataFrame
133 | #> features : 1
134 | #> extent : 328920.9, 329072.8, 4690670, 4690782 (xmin, xmax, ymin, ymax)
135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs
136 | #> variables : 1
137 | #> names : objectid
138 | #> value : 46 
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/man/shadowHeight.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/shadowHeight.R
3 | \name{shadowHeight}
4 | \alias{shadowHeight}
5 | \alias{shadowHeight,SpatialPoints-method}
6 | \alias{shadowHeight,Raster-method}
7 | \title{Shadow height calculation considering sun position and obstacles}
8 | \usage{
9 | \S4method{shadowHeight}{SpatialPoints}(
10 | location,
11 | obstacles,
12 | obstacles_height_field,
13 | solar_pos = solarpos2(location, time),
14 | time = NULL,
15 | b = 0.01,
16 | parallel = getOption("mc.cores"),
17 | filter_footprint = FALSE
18 | )
19 |
20 | \S4method{shadowHeight}{Raster}(
21 | location,
22 | obstacles,
23 | obstacles_height_field,
24 | solar_pos = solarpos2(pnt, time),
25 | time = NULL,
26 | b = 0.01,
27 | parallel = getOption("mc.cores"),
28 | filter_footprint = FALSE
29 | )
30 | }
31 | \arguments{
32 | \item{location}{A \code{SpatialPoints*} or \code{Raster*} object, specifying the location(s) for which to calculate shadow height}
33 |
34 | \item{obstacles}{A \code{SpatialPolygonsDataFrame} object specifying the obstacles outline}
35 |
36 | \item{obstacles_height_field}{Name of attribute in \code{obstacles} with extrusion height for each feature}
37 |
38 | \item{solar_pos}{A \code{matrix} with two columns representing sun position(s); first column is the solar azimuth (in decimal degrees from North), second column is sun elevation (in decimal degrees); rows represent different positions (e.g. at different times of day)}
39 |
40 | \item{time}{When \code{solar_pos} is unspecified, \code{time} can be passed to automatically calculate \code{solar_pos} based on the time and the centroid of \code{location}, using function \code{suntools::solarpos}. In such case \code{location} must have a defined CRS (not \code{NA}). The \code{time} value must be a \code{POSIXct} or \code{POSIXlt} object}
41 |
42 | \item{b}{Buffer size when joining intersection points with building outlines, to determine intersection height}
43 |
44 | \item{parallel}{Number of parallel processes or a predefined socket cluster. With \code{parallel=1} uses ordinary, non-parallel processing. Parallel processing is done with the \code{parallel} package}
45 |
46 | \item{filter_footprint}{Should the points be filtered using \code{shadowFootprint} before calculating shadow height? This can make the calculation faster when there are many point which are not shaded}
47 | }
48 | \value{
49 | Returned object is either a numeric \code{matrix} or a \code{Raster*} -
50 | \itemize{
51 | \item{If input \code{location} is a \code{SpatialPoints*}, then returned object is a \code{matrix}, where rows represent spatial locations (\code{location} features), columns represent solar positions (\code{solar_pos} rows) and values represent shadow height}
52 | \item{If input \code{location} is a \code{Raster*}, then returned object is a \code{RasterLayer} or \code{RasterStack} where layers represent solar positions (\code{solar_pos} rows) and pixel values represent shadow height}
53 | }
54 | In both cases the numeric values express shadow height -
55 | \itemize{
56 | \item{\code{NA} value means no shadow}
57 | \item{A \strong{valid number} expresses shadow height, in CRS units (e.g. meters)}
58 | \item{\code{Inf} means complete shadow (i.e. sun below horizon)}
59 | }
60 | }
61 | \description{
62 | This function calculates shadow height at given points or complete grid (\code{location}),
63 | taking into account:\itemize{
64 | \item{Obstacles outline (\code{obstacles}), given by a polygonal layer with a height attribute (\code{obstacles_height_field})}
65 | \item{Sun position (\code{solar_pos}), given by azimuth and elevation angles}
66 | }
67 | }
68 | \note{
69 | For a correct geometric calculation, make sure that:\itemize{
70 | \item{The layers \code{location} and \code{obstacles} are projected and in same CRS}
71 | \item{The values in \code{obstacles_height_field} of \code{obstacles} are given in the same distance units as the CRS (e.g. meters when using UTM)}
72 | }
73 | }
74 | \examples{
75 | # Single location
76 | # location = rgeos::gCentroid(build)
77 | location = as(sf::st_centroid(sf::st_union(sf::st_as_sf(build))), "Spatial")
78 | location_geo = matrix(c(34.7767978098526, 31.9665936050395), ncol = 2)
79 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
80 | solar_pos = suntools::solarpos(location_geo, time)
81 | plot(build, main = time)
82 | plot(location, add = TRUE)
83 | sun = shadow:::.sunLocation(location = location, sun_az = solar_pos[1,1], sun_elev = solar_pos[1,2])
84 | sun_ray = ray(from = location, to = sun)
85 | build_outline = as(build, "SpatialLinesDataFrame")
86 | # inter = rgeos::gIntersection(build_outline, sun_ray)
87 | inter = as(sf::st_intersection(sf::st_geometry(sf::st_as_sf(build_outline)),
88 | sf::st_as_sf(sun_ray)), "Spatial")
89 | plot(sun_ray, add = TRUE, col = "yellow")
90 | plot(inter, add = TRUE, col = "red")
91 | shadowHeight(
92 | location = location,
93 | obstacles = build,
94 | obstacles_height_field = "BLDG_HT",
95 | solar_pos = solar_pos
96 | )
97 |
98 | # Automatically calculating 'solar_pos' using 'time'
99 | proj4string(build) = CRS("EPSG:32636")
100 | proj4string(location) = CRS("EPSG:32636")
101 | shadowHeight(
102 | location = location,
103 | obstacles = build,
104 | obstacles_height_field = "BLDG_HT",
105 | time = time
106 | )
107 |
108 | \dontrun{
109 |
110 | # Two points - three times
111 | # location0 = rgeos::gCentroid(build)
112 | location0 = as(sf::st_centroid(sf::st_union(sf::st_as_sf(build))), "Spatial")
113 | location1 = raster::shift(location0, 0, -15)
114 | location2 = raster::shift(location0, -10, 20)
115 | locations = rbind(location1, location2)
116 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
117 | times = seq(from = time, by = "1 hour", length.out = 3)
118 | shadowHeight( ## Using 'solar_pos'
119 | location = locations,
120 | obstacles = build,
121 | obstacles_height_field = "BLDG_HT",
122 | solar_pos = suntools::solarpos(location_geo, times)
123 | )
124 | shadowHeight( ## Using 'time'
125 | location = locations,
126 | obstacles = build,
127 | obstacles_height_field = "BLDG_HT",
128 | time = times
129 | )
130 |
131 | # Grid - three times
132 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
133 | times = seq(from = time, by = "1 hour", length.out = 3)
134 | ext = as(raster::extent(build), "SpatialPolygons")
135 | r = raster::raster(ext, res = 2)
136 | proj4string(r) = proj4string(build)
137 | x = Sys.time()
138 | shadow1 = shadowHeight(
139 | location = r,
140 | obstacles = build,
141 | obstacles_height_field = "BLDG_HT",
142 | time = times,
143 | parallel = 3
144 | )
145 | y = Sys.time()
146 | y - x
147 | x = Sys.time()
148 | shadow2 = shadowHeight(
149 | location = r,
150 | obstacles = build,
151 | obstacles_height_field = "BLDG_HT",
152 | solar_pos = solarpos2(r, times),
153 | parallel = 3
154 | )
155 | y = Sys.time()
156 | y - x
157 | shadow = shadow1
158 | opar = par(mfrow = c(1, 3))
159 | for(i in 1:raster::nlayers(shadow)) {
160 | plot(shadow[[i]], col = grey(seq(0.9, 0.2, -0.01)), main = raster::getZ(shadow)[i])
161 | raster::contour(shadow[[i]], add = TRUE)
162 | plot(build, border = "red", add = TRUE)
163 | }
164 | par(opar)
165 |
166 | }
167 | }
168 |
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/docs/reference/toGMT.html:
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119 |
120 | The function transforms a POSIXct object in any given time zone to GMT.
121 |
122 |
123 | toGMT(time)
124 |
125 | Arguments
126 |
127 |
129 | time
130 | Time, a POSIXct object.
132 |
133 |
134 | Value
135 |
136 | A a POSIXct object, in GMT.
137 |
138 | Examples
139 | #> [1] "1999-01-01 10:00:00 GMT"
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/docs/reference/boston_sidewalk.html:
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9 | Polygonal layer of sidewalks in Boston — boston_sidewalk • shadow
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120 | A SpatialLinesDataFrame object representing sidewalks in Central Boston.
121 |
122 |
123 | boston_sidewalk
124 |
125 |
126 | Format
127 |
128 | A SpatialLinesDataFrame with 78 features.
129 |
130 | Examples
131 | boston_sidewalk
132 | #> class : SpatialLinesDataFrame
133 | #> features : 78
134 | #> extent : 328900, 329148, 4690530, 4690805 (xmin, xmax, ymin, ymax)
135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs
136 | #> variables : 3
137 | #> names : OBJECTID, TYPE, ShapeSTLen
138 | #> min values : 100131, CWALK-CL, 1.60299290715796
139 | #> max values : 99792, SWALK-CL, 314.796524738377 
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/docs/reference/build.html:
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123 | build
124 |
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126 | Format
127 |
128 | A SpatialPolygonsDataFrame with 4 features and 2 attributes:
129 | - build_id
Building ID
- BLDG_HT
Building height, in meters
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134 |
135 | Examples
136 | build
137 | #> class : SpatialPolygonsDataFrame
138 | #> features : 4
139 | #> extent : 667850.3, 667941.1, 3538084, 3538143 (xmin, xmax, ymin, ymax)
140 | #> crs : NA
141 | #> variables : 2
142 | #> names : build_id, BLDG_HT
143 | #> min values : 365, 19.07
144 | #> max values : 831, 22.73 
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/docs/reference/beersheva_elev.html:
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123 | beersheva_elev
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126 | Format
127 |
128 | A RasterLayer representing a grid of 1974 raster cells, each cell is a 30*30 meters rectangle. Data source is the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global dataset.
129 | References
130 |
131 | https://lta.cr.usgs.gov/SRTM1Arc
132 |
133 | Examples
134 | beersheva_elev
135 | #> class : RasterLayer
136 | #> dimensions : 47, 42, 1974 (nrow, ncol, ncell)
137 | #> resolution : 30, 30 (x, y)
138 | #> extent : 670636.6, 671896.6, 3461431, 3462841 (xmin, xmax, ymin, ymax)
139 | #> crs : +proj=utm +zone=36 +datum=WGS84 +units=m +no_defs
140 | #> source : memory
141 | #> names : elev_m
142 | #> values : 283.2574, 356.0146 (min, max)
143 | #> 
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/docs/reference/boston_build.html:
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9 | Polygonal layer of three buildings in Boston — boston_build • shadow
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114 | Polygonal layer of three buildings in Boston
115 | Source: R/data.R
116 | boston_build.Rd
117 |
118 |
119 |
120 | A SpatialPolygonsDataFrame object representing the outlines of three buildings located in Central Boston. The attribute height_m contains building height, in meters.
121 |
122 |
123 | boston_build
124 |
125 |
126 | Format
127 |
128 | A SpatialPolygonsDataFrame with 10 features and 4 attributes:
129 | - objectid
Building part ID
- build_id
Building ID
- part_floor
Number of floors for part
- height_m
Building height, in meters
135 |
136 |
137 | Examples
138 | boston_build
139 | #> class : SpatialPolygonsDataFrame
140 | #> features : 10
141 | #> extent : 328949.6, 329130.9, 4690552, 4690772 (xmin, xmax, ymin, ymax)
142 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs
143 | #> variables : 4
144 | #> names : objectid, build_id, part_floor, height_m
145 | #> min values : 99167, 1, 1, 3.631387178664
146 | #> max values : 123842, 3, 8, 240.240312 
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/R/SVFPnt.R:
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1 | .SVFPnt = function(
2 | location,
3 | obstacles,
4 | obstacles_height_field,
5 | res_angle = 5,
6 | b = 0.01
7 | ) {
8 |
9 | # Create rays
10 | angles = seq(0, 359.9999, res_angle)
11 | sun = mapply(
12 | .sunLocation,
13 | sun_az = angles,
14 | MoreArgs = list(
15 | location = location,
16 | sun_elev = 0
17 | )
18 | )
19 | rays = mapply(shadow::ray, MoreArgs = list(from = location), to = sun)
20 | rays$makeUniqueIDs = TRUE
21 | rays = do.call(sp::rbind.SpatialLines, rays)
22 |
23 | # # Add 'surface' outline to obstacles
24 | # surface = rgeos::gBuffer(location, width = 1000)
25 | # surface = SpatialPolygonsDataFrame(
26 | # Sr = surface,
27 | # data = data.frame(height = 0),
28 | # match.ID = FALSE
29 | # )
30 | # names(surface) = obstacles_height_field
31 | # surface = rbind(obstacles[, obstacles_height_field], surface)
32 |
33 | # 'obstacles' / 'surface' outline to 'lines' *** DEPENDS ON PACKAGE 'sp' ***
34 | # surface_outline = as(surface, "SpatialLinesDataFrame")
35 | obstacles_outline = as(obstacles, "SpatialLinesDataFrame")
36 |
37 | if(
38 |
39 | # 2D mode - if point is *on* a building then 'SVF=NA'
40 | (
41 | dimensions(location) == 2 & # 2D
42 | # rgeos::gIntersects(location, obstacles) # Intersects with 'obstacles'
43 | any(unlist(sf::st_intersects(sf::st_as_sf(location), sf::st_as_sf(obstacles))))
44 | ) ||
45 |
46 | # 3D mode - if point is *inside* building then 'SVF=NA'
47 | dimensions(location) == 3 & ( # 3D
48 | # rgeos::gIntersects(location, obstacles) && # Intersects with 'obstacles'
49 | any(unlist(sf::st_intersects(sf::st_as_sf(location), sf::st_as_sf(obstacles)))) &&
50 | coordinates(location)[, 3] < obstacles[location, ]@data[, obstacles_height_field] # Location z < Obstacle h
51 | )
52 | ) svf_final = NA else {
53 |
54 | svf = rep(NA, length(rays))
55 |
56 | for(i in 1:length(rays)) {
57 |
58 | # 'Line of sight' between sun and location
59 | ray1 = rays[i, ]
60 |
61 | # if(dimensions(location) == 2) {
62 |
63 | # 2D mode - Intersections with 'obstacles' outline
64 | # inter = rgeos::gIntersection(obstacles_outline, ray1)
65 | inter = sf::st_intersection(sf::st_geometry(sf::st_as_sf(obstacles_outline)), sf::st_geometry(sf::st_as_sf(ray1)))
66 | # }
67 |
68 | # if(dimensions(location) == 3) {
69 | #
70 | # # 3D mode - Intersections with 'surface' outline
71 | # inter = rgeos::gIntersection(surface_outline, ray1)
72 | #
73 | # }
74 |
75 | # No intersections means 'SVF=1'
76 | if(length(inter) == 0L) svf[i] = 1 else {
77 |
78 | # If some of the intersections are lines then convert to points
79 | # if(is(inter, "SpatialCollections")) {
80 | # lin = inter@lineobj
81 | # inter = inter@pointobj
82 | # for(lin_i in 1:length(lin)) {
83 | # lin_pnt = lin[lin_i, ]
84 | # lin_pnt = coordinates(lin_pnt)[[1]][[1]]
85 | # lin_pnt = sp::SpatialPoints(
86 | # lin_pnt,
87 | # proj4string = CRS(proj4string(inter))
88 | # )
89 | # inter = sp::rbind.SpatialPoints(inter, lin_pnt)
90 | # }
91 | if(length(grep("GEOMETRY", sf::st_geometry_type(inter))) > 0) {
92 |
93 | lin = sf::st_cast(sf::st_collection_extract(inter, "LINESTRING"), "POINT")
94 | inter = sf::st_collection_extract(inter, "POINT")
95 |
96 | inter = as(c(st_geometry(inter), st_geometry(lin)), "Spatial")
97 | }
98 |
99 | # Set row names
100 |
101 | inter = as(as(inter, "Spatial"), "SpatialPoints")
102 | row.names(inter) = 1:length(inter)
103 |
104 | # Extract 'obstacles' / 'surface' data for each intersection
105 | # if(dimensions(location) == 2) outline = obstacles_outline
106 | # if(dimensions(location) == 3) outline = surface_outline
107 | inter =
108 | SpatialPointsDataFrame(
109 | inter,
110 | sp::over(
111 | inter,
112 | # rgeos::gBuffer(obstacles_outline, byid = TRUE, width = b),
113 | as(sf::st_buffer(sf::st_as_sf(obstacles_outline), dist = b),
114 | "Spatial"),
115 | fn = max)
116 | )
117 |
118 | # Distance between examined location and intersections
119 | # inter$dist = rgeos::gDistance(inter, location, byid = TRUE)[1, ]
120 | inter$dist = sf::st_distance(sf::st_as_sf(inter), sf::st_as_sf(location))
121 |
122 | # Maximal angle of obstruction calculation
123 |
124 | # 2D mode - height difference equal to obstacles height
125 | if(dimensions(location) == 2) {
126 | inter$height_diff = inter@data[, obstacles_height_field]
127 | }
128 |
129 | # 3D mode - location height *subtracted* from obstacles height
130 | if(dimensions(location) == 3) {
131 | inter$height_diff = max(
132 | inter@data[, obstacles_height_field] - coordinates(location)[, 3],
133 | 0 # If location z > obstacles h then 'height_diff=0'
134 | )
135 | }
136 |
137 | inter$angle = rad2deg(atan(inter$height_diff / inter$dist))
138 | # inter$svf = 1 - inter$angle / 90
139 | inter$svf = 1 - sin(deg2rad(inter$angle))^2 # Gal & Unger 2014
140 | svf[i] = min(inter$svf)
141 |
142 | }
143 |
144 | }
145 |
146 | svf_final = mean(svf)
147 |
148 | }
149 |
150 | return(svf_final)
151 |
152 | }
153 |
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/R/data.R:
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1 | #' Polygonal layer of four buildings in Rishon
2 | #'
3 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of four buildings located in Rishon-Le-Zion. The attribute \code{BLDG_HT} contains building height, in meters.
4 | #'
5 | #' @format A \code{SpatialPolygonsDataFrame} with 4 features and 2 attributes:
6 | #' \describe{
7 | #' \item{build_id}{Building ID}
8 | #' \item{BLDG_HT}{Building height, in meters}
9 | #' }
10 | #' @examples
11 | #' build
12 | #' plot(build, axes = TRUE)
13 |
14 | "build"
15 |
16 | #' Polygonal layer of three buildings in Boston
17 | #'
18 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of three buildings located in Central Boston. The attribute \code{height_m} contains building height, in meters.
19 | #'
20 | #' @format A \code{SpatialPolygonsDataFrame} with 10 features and 4 attributes:
21 | #' \describe{
22 | #' \item{objectid}{Building part ID}
23 | #' \item{build_id}{Building ID}
24 | #' \item{part_floor}{Number of floors for part}
25 | #' \item{height_m}{Building height, in meters}
26 | #' }
27 | #' @examples
28 | #' boston_build
29 | #' plot(boston_build, axes = TRUE)
30 |
31 | "boston_build"
32 |
33 | #' Polygonal layer of 376 buildings in Beer-Sheva
34 | #'
35 | #' A \code{SpatialPolygonsDataFrame} object representing the outlines of 367 buildings in the Ramot neighborhood, Beer-Sheva. The attribute \code{height_m} contains building height, in meters.
36 | #'
37 | #' @format A \code{SpatialPolygonsDataFrame} with 10 features and 4 attributes:
38 | #' \describe{
39 | #' \item{build_id}{Building ID}
40 | #' \item{floors}{Number of floors for building}
41 | #' \item{apartments}{Number of apartments}
42 | #' \item{height_m}{Building height, in meters}
43 | #' \item{elev}{Elevation above sea level of building base, in meters}
44 | #' }
45 | #' @examples
46 | #' beersheva_build
47 | #' plot(beersheva_build, axes = TRUE)
48 |
49 | "beersheva_build"
50 |
51 | #' Polygonal layer of a building block in Boston
52 | #'
53 | #' A \code{SpatialPolygons} object representing the boundaries of a building block in Central Boston.
54 | #'
55 | #' @format A \code{SpatialPolygons} with a single feature.
56 | #' @examples
57 | #' boston_block
58 | #' plot(boston_block, axes = TRUE)
59 |
60 | "boston_block"
61 |
62 | #' Polygonal layer of a park in Boston
63 | #'
64 | #' A \code{SpatialPolygons} object representing the boundaries of a park in Central Boston.
65 | #'
66 | #' @format A \code{SpatialPolygons} with a single feature.
67 | #' @examples
68 | #' boston_park
69 | #' plot(boston_park, axes = TRUE)
70 |
71 | "boston_park"
72 |
73 | #' Polygonal layer of sidewalks in Boston
74 | #'
75 | #' A \code{SpatialLinesDataFrame} object representing sidewalks in Central Boston.
76 | #'
77 | #' @format A \code{SpatialLinesDataFrame} with 78 features.
78 | #' @examples
79 | #' boston_sidewalk
80 | #' plot(boston_sidewalk, axes = TRUE)
81 |
82 | "boston_sidewalk"
83 |
84 | #' Typical Meteorological Year (TMY) solar radiation in Tel-Aviv
85 | #'
86 | #' A table with hourly solar radiation estimates for a typical meteorological year in Tel-Aviv. \itemize{
87 | #' \item{\code{time} Time, as \code{character} in the \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, e.g. \code{"2000-01-01 06:00:00"}}, referring to local time
88 | #' \item{\code{sun_az} Sun azimuth, in decimal degrees from North}
89 | #' \item{\code{sun_elev} Sun elevation, in decimal degrees}
90 | #' \item{\code{solar_normal} Direct Normal Irradiance, in Wh/m^2}
91 | #' \item{\code{solar_diffuse} Diffuse Horizontal Irradiance, in Wh/m^2}
92 | #' \item{\code{dbt} Dry-bulb temperature, in Celsius degrees}
93 | #' \item{\code{ws} Wind speed, in m/s}
94 | #' }
95 | #'
96 | #' @format A \code{data.frame} with 8760 rows and 7 columns.
97 | #'
98 | #' @references
99 | #' \url{https://energyplus.net/weather-location/europe_wmo_region_6/ISR/ISR_Tel.Aviv-Bet.Dagan.401790_MSI}
100 | #' @examples
101 | #' head(tmy)
102 |
103 | "tmy"
104 |
105 | #' Typical Meteorological Year (TMY) solar radiation in Beer-Sheva
106 | #'
107 | #' A table with hourly solar radiation estimates for a typical meteorological year in Beer-Sheva. \itemize{
108 | #' \item{\code{time} Time, as \code{character} in the \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, e.g. \code{"2000-01-01 06:00:00"}}, referring to local time
109 | #' \item{\code{sun_az} Sun azimuth, in decimal degrees from North}
110 | #' \item{\code{sun_elev} Sun elevation, in decimal degrees}
111 | #' \item{\code{solar_normal} Direct Normal Irradiance, in Wh/m^2}
112 | #' \item{\code{solar_diffuse} Diffuse Horizontal Irradiance, in Wh/m^2}
113 | #' \item{\code{dbt} Dry-bulb temperature, in Celsius degrees}
114 | #' \item{\code{ws} Wind speed, in m/s}
115 | #' }
116 | #'
117 | #' @format A \code{data.frame} with 8760 rows and 7 columns.
118 | #'
119 | #' @references
120 | #' \url{https://energyplus.net/weather-location/europe_wmo_region_6/ISR/ISR_Beer.Sheva.401900_MSI}
121 | #' @examples
122 | #' head(tmy2)
123 |
124 | "tmy2"
125 |
126 | #' DEM of Ramot neighborhood, Beer-Sheva
127 | #'
128 | #' Digital Elevation Model (DEM) of Ramot neighborhood, Beer-Sheva. Raster values represent elevation above sea level, in meters.
129 | #'
130 | #' @format A \code{RasterLayer} representing a grid of 1974 raster cells, each cell is a 30*30 meters rectangle. Data source is the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global dataset.
131 | #'
132 | #' @references
133 | #' \url{https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1}
134 | #' @examples
135 | #' beersheva_elev
136 | #' plot(beersheva_elev)
137 |
138 | "beersheva_elev"
139 |
140 |
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119 | Polygonal layer of a building block in Boston
115 | Source:R/data.R
116 | boston_block.Rd
120 |
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123 | A SpatialPolygons object representing the boundaries of a building block in Central Boston.
boston_block
124 |
125 |
126 | Format
127 | 128 |A SpatialPolygons with a single feature.
Examples
131 |137 |boston_block 132 |#> class : SpatialPolygons 133 | #> features : 1 134 | #> extent : 328903.9, 329144.9, 4690539, 4690799 (xmin, xmax, ymin, ymax) 135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs
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/man/shadowHeight.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/shadowHeight.R
3 | \name{shadowHeight}
4 | \alias{shadowHeight}
5 | \alias{shadowHeight,SpatialPoints-method}
6 | \alias{shadowHeight,Raster-method}
7 | \title{Shadow height calculation considering sun position and obstacles}
8 | \usage{
9 | \S4method{shadowHeight}{SpatialPoints}(
10 | location,
11 | obstacles,
12 | obstacles_height_field,
13 | solar_pos = solarpos2(location, time),
14 | time = NULL,
15 | b = 0.01,
16 | parallel = getOption("mc.cores"),
17 | filter_footprint = FALSE
18 | )
19 |
20 | \S4method{shadowHeight}{Raster}(
21 | location,
22 | obstacles,
23 | obstacles_height_field,
24 | solar_pos = solarpos2(pnt, time),
25 | time = NULL,
26 | b = 0.01,
27 | parallel = getOption("mc.cores"),
28 | filter_footprint = FALSE
29 | )
30 | }
31 | \arguments{
32 | \item{location}{A \code{SpatialPoints*} or \code{Raster*} object, specifying the location(s) for which to calculate shadow height}
33 |
34 | \item{obstacles}{A \code{SpatialPolygonsDataFrame} object specifying the obstacles outline}
35 |
36 | \item{obstacles_height_field}{Name of attribute in \code{obstacles} with extrusion height for each feature}
37 |
38 | \item{solar_pos}{A \code{matrix} with two columns representing sun position(s); first column is the solar azimuth (in decimal degrees from North), second column is sun elevation (in decimal degrees); rows represent different positions (e.g. at different times of day)}
39 |
40 | \item{time}{When \code{solar_pos} is unspecified, \code{time} can be passed to automatically calculate \code{solar_pos} based on the time and the centroid of \code{location}, using function \code{suntools::solarpos}. In such case \code{location} must have a defined CRS (not \code{NA}). The \code{time} value must be a \code{POSIXct} or \code{POSIXlt} object}
41 |
42 | \item{b}{Buffer size when joining intersection points with building outlines, to determine intersection height}
43 |
44 | \item{parallel}{Number of parallel processes or a predefined socket cluster. With \code{parallel=1} uses ordinary, non-parallel processing. Parallel processing is done with the \code{parallel} package}
45 |
46 | \item{filter_footprint}{Should the points be filtered using \code{shadowFootprint} before calculating shadow height? This can make the calculation faster when there are many point which are not shaded}
47 | }
48 | \value{
49 | Returned object is either a numeric \code{matrix} or a \code{Raster*} -
50 | \itemize{
51 | \item{If input \code{location} is a \code{SpatialPoints*}, then returned object is a \code{matrix}, where rows represent spatial locations (\code{location} features), columns represent solar positions (\code{solar_pos} rows) and values represent shadow height}
52 | \item{If input \code{location} is a \code{Raster*}, then returned object is a \code{RasterLayer} or \code{RasterStack} where layers represent solar positions (\code{solar_pos} rows) and pixel values represent shadow height}
53 | }
54 | In both cases the numeric values express shadow height -
55 | \itemize{
56 | \item{\code{NA} value means no shadow}
57 | \item{A \strong{valid number} expresses shadow height, in CRS units (e.g. meters)}
58 | \item{\code{Inf} means complete shadow (i.e. sun below horizon)}
59 | }
60 | }
61 | \description{
62 | This function calculates shadow height at given points or complete grid (\code{location}),
63 | taking into account:\itemize{
64 | \item{Obstacles outline (\code{obstacles}), given by a polygonal layer with a height attribute (\code{obstacles_height_field})}
65 | \item{Sun position (\code{solar_pos}), given by azimuth and elevation angles}
66 | }
67 | }
68 | \note{
69 | For a correct geometric calculation, make sure that:\itemize{
70 | \item{The layers \code{location} and \code{obstacles} are projected and in same CRS}
71 | \item{The values in \code{obstacles_height_field} of \code{obstacles} are given in the same distance units as the CRS (e.g. meters when using UTM)}
72 | }
73 | }
74 | \examples{
75 | # Single location
76 | # location = rgeos::gCentroid(build)
77 | location = as(sf::st_centroid(sf::st_union(sf::st_as_sf(build))), "Spatial")
78 | location_geo = matrix(c(34.7767978098526, 31.9665936050395), ncol = 2)
79 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
80 | solar_pos = suntools::solarpos(location_geo, time)
81 | plot(build, main = time)
82 | plot(location, add = TRUE)
83 | sun = shadow:::.sunLocation(location = location, sun_az = solar_pos[1,1], sun_elev = solar_pos[1,2])
84 | sun_ray = ray(from = location, to = sun)
85 | build_outline = as(build, "SpatialLinesDataFrame")
86 | # inter = rgeos::gIntersection(build_outline, sun_ray)
87 | inter = as(sf::st_intersection(sf::st_geometry(sf::st_as_sf(build_outline)),
88 | sf::st_as_sf(sun_ray)), "Spatial")
89 | plot(sun_ray, add = TRUE, col = "yellow")
90 | plot(inter, add = TRUE, col = "red")
91 | shadowHeight(
92 | location = location,
93 | obstacles = build,
94 | obstacles_height_field = "BLDG_HT",
95 | solar_pos = solar_pos
96 | )
97 |
98 | # Automatically calculating 'solar_pos' using 'time'
99 | proj4string(build) = CRS("EPSG:32636")
100 | proj4string(location) = CRS("EPSG:32636")
101 | shadowHeight(
102 | location = location,
103 | obstacles = build,
104 | obstacles_height_field = "BLDG_HT",
105 | time = time
106 | )
107 |
108 | \dontrun{
109 |
110 | # Two points - three times
111 | # location0 = rgeos::gCentroid(build)
112 | location0 = as(sf::st_centroid(sf::st_union(sf::st_as_sf(build))), "Spatial")
113 | location1 = raster::shift(location0, 0, -15)
114 | location2 = raster::shift(location0, -10, 20)
115 | locations = rbind(location1, location2)
116 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
117 | times = seq(from = time, by = "1 hour", length.out = 3)
118 | shadowHeight( ## Using 'solar_pos'
119 | location = locations,
120 | obstacles = build,
121 | obstacles_height_field = "BLDG_HT",
122 | solar_pos = suntools::solarpos(location_geo, times)
123 | )
124 | shadowHeight( ## Using 'time'
125 | location = locations,
126 | obstacles = build,
127 | obstacles_height_field = "BLDG_HT",
128 | time = times
129 | )
130 |
131 | # Grid - three times
132 | time = as.POSIXct("2004-12-24 13:30:00", tz = "Asia/Jerusalem")
133 | times = seq(from = time, by = "1 hour", length.out = 3)
134 | ext = as(raster::extent(build), "SpatialPolygons")
135 | r = raster::raster(ext, res = 2)
136 | proj4string(r) = proj4string(build)
137 | x = Sys.time()
138 | shadow1 = shadowHeight(
139 | location = r,
140 | obstacles = build,
141 | obstacles_height_field = "BLDG_HT",
142 | time = times,
143 | parallel = 3
144 | )
145 | y = Sys.time()
146 | y - x
147 | x = Sys.time()
148 | shadow2 = shadowHeight(
149 | location = r,
150 | obstacles = build,
151 | obstacles_height_field = "BLDG_HT",
152 | solar_pos = solarpos2(r, times),
153 | parallel = 3
154 | )
155 | y = Sys.time()
156 | y - x
157 | shadow = shadow1
158 | opar = par(mfrow = c(1, 3))
159 | for(i in 1:raster::nlayers(shadow)) {
160 | plot(shadow[[i]], col = grey(seq(0.9, 0.2, -0.01)), main = raster::getZ(shadow)[i])
161 | raster::contour(shadow[[i]], add = TRUE)
162 | plot(build, border = "red", add = TRUE)
163 | }
164 | par(opar)
165 |
166 | }
167 | }
168 |
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/docs/reference/toGMT.html:
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122 |
123 | A SpatialPolygons object representing the boundaries of a park in Central Boston.
boston_park
124 |
125 |
126 | Format
127 | 128 |A SpatialPolygons with a single feature.
Examples
131 |140 |boston_park 132 |#> class : SpatialPolygonsDataFrame 133 | #> features : 1 134 | #> extent : 328920.9, 329072.8, 4690670, 4690782 (xmin, xmax, ymin, ymax) 135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs 136 | #> variables : 1 137 | #> names : objectid 138 | #> value : 46
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/docs/reference/boston_sidewalk.html:
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122 |
123 | The function transforms a POSIXct object in any given time zone to GMT.
toGMT(time)124 | 125 |
Arguments
126 || time | 130 |Time, a |
131 |
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Value
135 | 136 |A a POSIXct object, in GMT.
Examples
139 |143 |#> [1] "1999-01-01 10:00:00 GMT"142 |
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122 |
123 | A SpatialLinesDataFrame object representing sidewalks in Central Boston.
boston_sidewalk
124 |
125 |
126 | Format
127 | 128 |A SpatialLinesDataFrame with 78 features.
Examples
131 |141 |boston_sidewalk 132 |#> class : SpatialLinesDataFrame 133 | #> features : 78 134 | #> extent : 328900, 329148, 4690530, 4690805 (xmin, xmax, ymin, ymax) 135 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs 136 | #> variables : 3 137 | #> names : OBJECTID, TYPE, ShapeSTLen 138 | #> min values : 100131, CWALK-CL, 1.60299290715796 139 | #> max values : 99792, SWALK-CL, 314.796524738377
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/docs/reference/beersheva_elev.html:
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123 | A SpatialPolygonsDataFrame object representing the outlines of four buildings located in Rishon-Le-Zion. The attribute BLDG_HT contains building height, in meters.
build
124 |
125 |
126 | Format
127 | 128 |A SpatialPolygonsDataFrame with 4 features and 2 attributes:
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129 |
- build_id
Building ID
130 | - BLDG_HT
Building height, in meters
131 |
132 |
Examples
136 |146 |build 137 |#> class : SpatialPolygonsDataFrame 138 | #> features : 4 139 | #> extent : 667850.3, 667941.1, 3538084, 3538143 (xmin, xmax, ymin, ymax) 140 | #> crs : NA 141 | #> variables : 2 142 | #> names : build_id, BLDG_HT 143 | #> min values : 365, 19.07 144 | #> max values : 831, 22.73
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/docs/reference/boston_build.html:
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123 | Digital Elevation Model (DEM) of Ramot neighborhood, Beer-Sheva. Raster values represent elevation above sea level, in meters.
121 |beersheva_elev
124 |
125 |
126 | Format
127 | 128 |A RasterLayer representing a grid of 1974 raster cells, each cell is a 30*30 meters rectangle. Data source is the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global dataset.
References
130 | 131 |https://lta.cr.usgs.gov/SRTM1Arc
132 | 133 |Examples
134 |145 |beersheva_elev 135 |#> class : RasterLayer 136 | #> dimensions : 47, 42, 1974 (nrow, ncol, ncell) 137 | #> resolution : 30, 30 (x, y) 138 | #> extent : 670636.6, 671896.6, 3461431, 3462841 (xmin, xmax, ymin, ymax) 139 | #> crs : +proj=utm +zone=36 +datum=WGS84 +units=m +no_defs 140 | #> source : memory 141 | #> names : elev_m 142 | #> values : 283.2574, 356.0146 (min, max) 143 | #>
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118 |
119 | Polygonal layer of three buildings in Boston
115 | Source:R/data.R
116 | boston_build.Rd
120 |
122 |
123 | A SpatialPolygonsDataFrame object representing the outlines of three buildings located in Central Boston. The attribute height_m contains building height, in meters.
boston_build
124 |
125 |
126 | Format
127 | 128 |A SpatialPolygonsDataFrame with 10 features and 4 attributes:
-
129 |
- objectid
Building part ID
130 | - build_id
Building ID
131 | - part_floor
Number of floors for part
132 | - height_m
Building height, in meters
133 |
134 |
Examples
138 |148 |boston_build 139 |#> class : SpatialPolygonsDataFrame 140 | #> features : 10 141 | #> extent : 328949.6, 329130.9, 4690552, 4690772 (xmin, xmax, ymin, ymax) 142 | #> crs : +proj=utm +zone=19 +datum=WGS84 +units=m +no_defs 143 | #> variables : 4 144 | #> names : objectid, build_id, part_floor, height_m 145 | #> min values : 99167, 1, 1, 3.631387178664 146 | #> max values : 123842, 3, 8, 240.240312