andrewmaclachlan / casa0005repo Goto Github PK

View Code? Open in Web Editor NEW

58.0 58.0 71.0 604.52 MB

Bookdown :book: for CASA0005 practicals: https://andrewmaclachlan.github.io/CASA0005repo/

License: Creative Commons Attribution Share Alike 4.0 International

Dockerfile 0.01% Shell 0.01% JavaScript 0.01% HTML 99.22% CSS 0.13% TeX 0.38% R 0.26%

analytics data-science education gis r spatial

casa0005repo's Introduction

Hi there 👋 I'm Andy MacLachlan

📖 About me

I'm an Associate Professor in spatial data science and visualisation at the Centre for Advanced Spatial Analysis (CASA), University College London.
My expertise lies in remote sensing, although in recent years this has expanded to any project using spatial data and geographical information science.
R is my language of choice, but i also use PostGIS and Google Earth Engine

"A jack of all trades is a master of none, but oftentimes better than a master of one"

🏗️ What i'm doing

👨‍🏫 Teach

I supervise doctoral researchers, MSc students and lead two MSc modules:

I run CASA's MSc Urban Spatial Science programme

🔎 Research

My research revovles around developing data-driven urban development recommendations to enhance future sustainability and optimise resource investment. This has included work on Urban Heat Islands and more recently water accessibility in Tanzania using PostGIS.

If it's not applied, i'm not interested.

💰 Scholarships / other

I apply and maintain a range of scholarship funding for our MSc, including:

I teach at other institutions a few times a year, this has included the MSc Modelling for Global Health and the University of Oxford and a guest workshop at IIT Hyderabad.

casa0005repo's People

Contributors

Stargazers

Watchers

casa0005repo's Issues

spatstat sf to ppp

Better way to convert to ppp than sf to sp https://www.paulamoraga.com/book-spatial/the-spatstat-package.html#from-sf-to-ppp

Update pipe

Change pipe from %>% to |>

Better maps in week 5

Take inspiration from https://github.com/BlakeRMills/30DayMapChallenge/tree/main/2023 or 30 day map challenge.

Consider less code in this week and more focus on mapping.

Do we need functions here?

DBSCAN code

Updated from MSc student

I wrote a bit of code for creating convex hulls for the dbscan output, which are a bit more nicely shaped than those in the practical, maybe someone will find it useful 🙂
convex hulls to wrap around points

chulls <- data.frame() for (cluster in 1:max(BluePlaquesSubPoints$dbcluster)) { cluster_data <- BluePlaquesSubPoints %>% filter(dbcluster == cluster) ch <- chull(cluster_data$coords.x1, cluster_data$coords.x2) chulls <- chulls %>% bind_rows(cluster_data[c(ch), ]) }

Add key advice for point pattern analysis

Write up notes on key advice like the key advice on GWR

Tips on Chap7 data preprocessing

Currently I'm learning this tutorial on my own, and I find that in 7.7.2, it takes a long time to resample such big data. Therefore I suggest we do the clipping in 7.7.3 first, and then resample. This may save some time. Will problem arise in it? Thanks.

My codes:


library(pacman)
p_load(sp,raster,rgeos,rgdal,rasterVis,raster,fs,sf,tidyverse)

# read data
manchester_boundary <- st_read("data/manchester_boundary_download/Manchester_boundary.shp")
listlandsat <- dir("data/LC08_L1TP_203023_20190513_20190521_01_T1",
    pattern = "[B123456790].TIF",full.names = T) %>% stack()
#check they have the same Coordinate Reference System (CRS)
crs(manchester_boundary)
crs(listlandsat)

# get only Manchester
lsatmask <- listlandsat %>%
  # now crop our temp data to the extent
  crop(.,manchester_boundary)%>%
  mask(.,  manchester_boundary)

# handle 8th band
b8list = dir("data/LC08_L1TP_203023_20190513_20190521_01_T1",
    pattern = "[B8].TIF",full.names = T) %>% 
  raster()

## ngb is a nearest neighbour sampling method
b8correct <- b8list%>%
  # now crop our temp data to the extent
  crop(.,manchester_boundary)%>%
  mask(.,  manchester_boundary)%>%
  resample(., lsatmask$LC08_L1TP_203023_20190513_20190521_01_T1_B1, 
           method = "ngb") 

lsatmask <- lsatmask %>%
  addLayer(., b8correct)

raster::compareRaster(listlandsat$LC08_L1TP_203023_20190513_20190521_01_T1_B1,
                      listlandsat$LC08_L1TP_203023_20190513_20190521_01_T1_B8)




# add mask to the filenames within the raster stack

names(lsatmask) <- names(lsatmask)%>%
  str_c(., 
        "mask", 
        sep="_")

Thank you for your contribution. I've learned a lot from this course.