## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(spotoroo) ## ----------------------------------------------------------------------------- str(hotspots) ## ----------------------------------------------------------------------------- library(ggplot2) if (requireNamespace("sf", quietly = TRUE)) { plot_vic_map() + geom_point(data = hotspots, aes(lon, lat), col = "red") } ## ----echo = FALSE------------------------------------------------------------- tab <- data.frame(Arguments = c("`hotspots`", "`lon`", "`lat`", "`obsTime`"), Description = c("the object that contains the dataset", "the name of the longitude column", "the name of the latitude column", "the name of the observed time column")) knitr::kable(tab) ## ----echo = FALSE------------------------------------------------------------- tab <- data.frame(Arguments = c("`activeTime`", "`adjDist`", "`minPts`", "`minTime`"), Description = c("the time tolerance", "the distance tolerance", "the minimum number of hot spots", "the minimum length of time")) knitr::kable(tab) ## ----echo = FALSE------------------------------------------------------------- tab <- data.frame(Arguments = c("`ignitionCenter`"), Description = c("method of the calculation of the ignition points")) knitr::kable(tab) ## ----echo = FALSE------------------------------------------------------------- tab <- data.frame(Arguments = c("`timeUnit`", "`timeStep`"), Description = c("the unit of time", "the number of time unit one time index contains")) knitr::kable(tab) ## ----------------------------------------------------------------------------- result <- hotspot_cluster(hotspots = hotspots, lon = "lon", lat = "lat", obsTime = "obsTime", activeTime = 24, adjDist = 3000, minPts = 4, minTime = 3, ignitionCenter = "mean", timeUnit = "h", timeStep = 1) ## ----------------------------------------------------------------------------- result ## ----------------------------------------------------------------------------- head(result$hotspots, 2) head(result$ignition, 2) ## ----eval = FALSE------------------------------------------------------------- # # Merge the `hotspots` and `ignition` dataset # merged_result <- extract_fire(result, cluster = "all", noise = TRUE) ## ----eval = FALSE------------------------------------------------------------- # # Merge the `hotspots` and `ignition` dataset # # Select cluster 2 and 3 and filter out noise # cluster_2_and_3 <- extract_fire(result, cluster = c(2, 3), noise = FALSE) ## ----echo = FALSE------------------------------------------------------------- tab <- expand.grid(activeTime = seq(6, 48, 6), adjDist = seq(500, 4000, 500)) tab$noise_prop <- c(0.320560748, 0.282242991, 0.235514019, 0.133644860, 0.129906542, 0.129906542, 0.126168224, 0.118691589, 0.320560748, 0.282242991, 0.235514019, 0.133644860, 0.129906542, 0.129906542, 0.126168224, 0.118691589, 0.320560748, 0.282242991, 0.235514019, 0.133644860, 0.129906542, 0.129906542, 0.126168224, 0.118691589, 0.154205607, 0.134579439, 0.109345794, 0.026168224, 0.026168224, 0.026168224, 0.026168224, 0.021495327, 0.086915888, 0.075700935, 0.055140187, 0.011214953, 0.011214953, 0.011214953, 0.011214953, 0.011214953, 0.081308411, 0.070093458, 0.049532710, 0.009345794, 0.009345794, 0.009345794, 0.009345794, 0.009345794, 0.081308411, 0.070093458, 0.049532710, 0.009345794, 0.009345794, 0.009345794, 0.009345794, 0.009345794, 0.079439252, 0.061682243, 0.049532710, 0.009345794, 0.009345794, 0.009345794, 0.009345794, 0.009345794) ## ----------------------------------------------------------------------------- ggplot(tab) + geom_line(aes(adjDist, noise_prop, color = as.factor(activeTime))) + ylab("Noise Propotion") + labs(col = "activeTime") + theme_minimal() + scale_x_continuous(breaks = seq(500, 4000, 500)) ## ----------------------------------------------------------------------------- ggplot(tab) + geom_line(aes(activeTime, noise_prop, color = as.factor(adjDist))) + ylab("Noise Propotion") + labs(col = "adjDist") + theme_minimal() + scale_x_continuous(breaks = seq(6, 48, 6)) ## ----------------------------------------------------------------------------- summary_spotoroo(result) ## ----eval = FALSE------------------------------------------------------------- # summary_spotoroo(result, cluster = c(1, 3, 4)) ## ----eval = FALSE------------------------------------------------------------- # summary(result) # summary(result, cluster = c(1, 3, 4)) ## ----------------------------------------------------------------------------- plot_spotoroo(result, type = "def") ## ----------------------------------------------------------------------------- plot_spotoroo(result, type = "timeline") ## ----------------------------------------------------------------------------- plot_spotoroo(result, type = "mov", step = 6) ## ----------------------------------------------------------------------------- if (requireNamespace("sf", quietly = TRUE)) { plot_spotoroo(result, bg = plot_vic_map()) } ## ----------------------------------------------------------------------------- if (requireNamespace("sf", quietly = TRUE)) { plot_spotoroo(result, type = "mov", bg = plot_vic_map(), step = 6) } ## ----eval = FALSE------------------------------------------------------------- # plot(result) # plot(result, type = "timeline") # plot(result, type = "mov") # plot(result, bg = plot_vic_map()) # plot(result, type = "mov", bg = plot_vic_map())