Part I: Tutorials form class

This sections mainly focus on following the instructions of the lab tutorial and trying out all the code.

Loading lib

library(ggplot2)
library(sf)

## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggspatial)
library(grid)

library(cowplot)

## 
## 载入程辑包：'cowplot'
## 
## The following object is masked from 'package:lubridate':
## 
##     stamp

Start Loading Data

boulder = st_read("Data/BoulderSocialMedia.shp")

## Reading layer `BoulderSocialMedia' from data source 
##   `D:\UMich\Fall23\EAS648\Repo\Lab\01\Data\BoulderSocialMedia.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 55519 features and 12 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -788775 ymin: 1917813 xmax: -780555 ymax: 1930053
## Projected CRS: NAD_1983_Albers

head(boulder)

## Simple feature collection with 6 features and 12 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -786099 ymin: 1929916 xmax: -786099 ymax: 1929916
## Projected CRS: NAD_1983_Albers
##           id     DB   extent Climb_dist TrailH_Dis NatMrk_Dis Trails_dis
## 1 6517284333 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
## 2 6517281191 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
## 3 6517278961 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
## 4 6517276295 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
## 5 6517274727 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
## 6 6517272539 Flickr 421678.2   1973.108   2368.567   2451.633   49.73422
##   Bike_dis PrarDg_Dis PT_Elev Hydro_dis Street_dis                geometry
## 1 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)
## 2 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)
## 3 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)
## 4 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)
## 5 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)
## 6 1437.134   1942.125    2064   1359.75   193.9165 POINT (-786099 1929916)

Plot the variables

ggplot() +
    geom_sf(data =boulder, fill = NA, alpha = .2) +
    theme_bw()

Reproject the data

Project the data using an appropriate projection for Colorado. Use the epsg.io website for choosing the an appropriate projection

# transforming the current projection
boulder = st_transform(boulder, 26753)
ggplot() +
    geom_sf(data =boulder,
    fill = NA, alpha = .2) +
    theme_bw()

Changing Symbologies

Adding ‘Gradient colour scales’ in ggplot2. Here is the documentation of these options https://ggplot2.tidyverse.org/reference/scale_gradient.html.

# show elevation in color based on PT_Elev field
ggplot() +
    geom_sf(data =boulder, aes(color=PT_Elev),
    fill = NA, alpha = .2) +
    theme_bw()

Using another color scale:

# using scale_colour_gradientn
ggplot() +
    geom_sf(data =boulder, aes(color=PT_Elev),
    fill = NA, alpha = .2) +
  scale_colour_gradientn(colours = terrain.colors(10)) +  
  theme_bw()

Select Data

# library(dplyer)
boulder %>%
    mutate(high_elev = ifelse(PT_Elev >= 2200, TRUE, FALSE))%>% 
ggplot() +
  geom_sf(aes(color=high_elev),
    fill = NA, alpha = .2)  +  
  theme_bw()

Create boxplot

boulder %>%
  filter(DB ==  'Pano' | DB == 'Flickr') %>%
  ggplot(aes(x=DB, y=Street_dis)) + 
  geom_boxplot()

Additional Tools

library(sf)
library(ggspatial)
library(viridis)

## 载入需要的程辑包：viridisLite

ggplot() +
    geom_sf(data = boulder, aes(color=PT_Elev),
    fill = NA, alpha = .2) + 
    scale_colour_gradientn(colours = magma(10))

Plot discrete values:

summary(boulder$DB)

##    Length     Class      Mode 
##     55519 character character

p <- ggplot() +
  annotation_spatial(boulder) +
  layer_spatial(boulder, aes(col = DB))
p + scale_color_brewer(palette = "Dark2")

TMaps

library(tmap)

## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')

## Add the data - these are specific to the vector or raster
tm_shape(boulder) + 
  ## which variable, is there a class interval, palette, and other options
  tm_symbols(col='PT_Elev', 
             style='quantile', 
             palette = 'YlOrRd',
             border.lwd = NA,
             size = 0.1)

Adding interactive map

## here we are using a simple dataset of the world 
# tmap_mode("plot")
data("World")
tm_shape(World) +
    tm_polygons("gdp_cap_est", style='quantile', legend.title = "GDP Per Capita Estimate")

Part II: Assignment Tasks

Questions:

Discuss the advantages and challenges associated with an open data science approach. Provide an example based on this week’s reading. (1-2 paragraphs)

Answer:

An open data science approach, as exemplified by the potential release of the extensive dataset from President Obama’s election campaigns, offers several advantages. Firstly, it promotes transparency, which is fundamental in ensuring the credibility of research findings. This dataset, in particular, would offer invaluable insights into the intricate details of American societal, political, and economic dynamics, serving as a robust foundation for myriad research questions. Moreover, collaborative, open-source environments foster innovation, as they allow multiple researchers from diverse backgrounds to cross-pollinate ideas, refine methodologies, and advance knowledge at an accelerated pace.

However, such an approach is not without challenges. Chief among these is the risk of privacy breaches and potential misuse of sensitive information. Given that the dataset encompasses detailed personal information of voters, there’s a possibility that the data could be de-anonymized, violating individual privacy rights. Additionally, the sheer volume and complexity of open datasets can often lead to misinterpretation or misleading conclusions, especially when handled by those without the necessary domain expertise. Lastly, concerns related to data quality, accuracy, and bias can emerge, especially when integrating data from various sources like commercial data aggregators and social media sites. Ensuring consistent data integrity and addressing inherent biases would be crucial to derive meaningful and trustworthy insights.

Creating a Map

Task: Create a markdown document that showcases an analysis of this week’s data or any other dataset of your choice. Include descriptive text that explains your analysis, and incorporate figures and geovisualizations.

Answer:

For the labs assignment, I continue to use the boulder dataset.

Map Goal: Create a interest map for tourists, high light all the points that is within 50 meters of climbing wall, Natural Landmark or prairie dog mounds

# Step 1: create a new column indicating the nearest POI type
main_plot = boulder %>%
    mutate(
      near_poi = case_when(
        Climb_dist <= 50 ~ "Climing Wall",
        NatMrk_Dis <= 50 ~ "Natural Landmark",
        PrarDg_Dis <= 50 ~ "Prairie Dog Mounds",
        TRUE ~ "Other")
     )%>%

# using ggplot to plot the map
ggplot() +
  geom_sf(aes(color=near_poi),
    fill = NA)  +  
  
  # adding title
  ggtitle("Tourist Map of Interest in Boulder, Colorado") + 
  
  # altering the legend title
  labs(color = "POI In 50 Meters:") +
  
  # adding scale bar
  annotation_scale(location = "br", width_hint = 0.5) +
  
  # adding north arrow
  annotation_north_arrow(location = "tr", which_north = "true", 
                         style = north_arrow_fancy_orienteering()) +
  # adding theme: bw
  theme_bw()

# Create grobs annotation
label_text <- "Author: Haolin Li\nGCS: NAD 1983\nPCS: NAD27/Colorado North\nDate: 10/29/2023"
text_grob <- textGrob(label = label_text, 
                     hjust = 1, vjust = 0, 
                     gp = gpar(fontsize = 4))

text_grob <- editGrob(text_grob,x = unit(0.99, "npc"), y = unit(0.08, "npc"))

# adding annotaion to main plot
main_plot <- main_plot + 
  annotation_custom(grob = text_grob)

# print the map with annotation
print(main_plot)

Tourist attractiveness score map

Here I want to create a second plot for showing how attractive a point is to tourists. To calculate the score, I used the following method: The score of each points adds 10 for each condition it meets:

Within 10 meters of Climbing Wall

Within 10 meters of Natural Landmark

Within 10 meters of Prairie Dog Mounds

# Step 2: create a new column to score the attractiveness
main_plot = boulder %>%
  mutate(attr_score = 10 * ifelse(Climb_dist < 50, 1, 0) +
                     10 * ifelse(NatMrk_Dis < 50, 1, 0) +
                     10 * ifelse(PrarDg_Dis < 50, 1, 0)
         )%>%

# using ggplot to plot the map
ggplot() +
  geom_sf(aes(color=attr_score),
    fill = NA)  +  
  
  # adding title
  ggtitle("Attractiveness Score in Boulder, Colorado") + 
  
  # altering the legend title
  labs(color = "Score") +
  
  # adding scale bar
  annotation_scale(location = "br", width_hint = 0.5) +
  
  # adding north arrow
  annotation_north_arrow(location = "tr", which_north = "true", 
                         style = north_arrow_fancy_orienteering()) +
  # adding theme: bw
  theme_bw()

# Create grobs annotation
label_text <- "Author: Haolin Li\nGCS: NAD 1983\nPCS: NAD27/Colorado North\nDate: 10/29/2023"
text_grob <- textGrob(label = label_text, 
                     hjust = 1, vjust = 0, 
                     gp = gpar(fontsize = 4))

text_grob <- editGrob(text_grob,x = unit(0.99, "npc"), y = unit(0.08, "npc"))

# adding annotaion to main plot
main_plot <- main_plot + 
  annotation_custom(grob = text_grob)

# print the map with annotation
print(main_plot)

Visualize the attribution of attractiveness score

# Creating a pie chart to visualize the distribution of the score
# 假设您的数据框叫df
as_counts = table(mutate(boulder, attr_score = 10 * ifelse(Climb_dist < 50, 1, 0) +
                     10 * ifelse(NatMrk_Dis < 50, 1, 0) +
                     10 * ifelse(PrarDg_Dis < 50, 1, 0))$attr_score)

as_proportions = as_counts / sum(as_counts)

as_labels = paste(names(as_proportions), ": ", round(as_proportions*100, 2), "%", sep="")

pie(as_counts, main = "Pie Chart of Attractiveness Score", labels = as_labels)

Analysing the Result:

As we can see in the first and second map, Boulder is a place with abundant natural tourism resouces. Most of the attraction of comes from being inside or next to a natural landmark. With the natural landmarks are several climbing walls, which mainly exists in the center of the area. The southeastern part of the area also shows high attractiveness with interesting prairie dog mounds and second largest natural landmark. The pie chart shows that almost 15% of the points in this area is within the 50 meters radius of a tourist attraction.

Bonus: Utilizing Git

Task: Host a HTML of your RMD as a Website on GitHub

Github repository: https://github.com/HumblePasty/EAS648

Screenshot of Commits:

Commit Screenshot

Github Webpage of the Project:

Visit My EAS-648 Webpage:https://humblepasty.github.io/EAS648/

EAS648_Lab01_haolinli

Haolin Li (haolinli@umich.edu)

2023-10-19

Part I: Tutorials form class

Loading lib

Start Loading Data

Plot the variables

Reproject the data

Changing Symbologies

Using another color scale:

Select Data

Create boxplot

Additional Tools

Plot discrete values:

TMaps

Adding interactive map

Part II: Assignment Tasks

Questions:

Creating a Map

Tourist attractiveness score map

Visualize the attribution of attractiveness score

Analysing the Result:

Bonus: Utilizing Git

Screenshot of Commits:

Github Webpage of the Project: