rossihou Goto Github PK

followers: 5.0 following: 4.0 repos: 27.0 gists: 0.0

Name: Rossi_HOU

Type: User

Company: Curtin University

Bio: Do fun things. 跪着要饭

Introduction

Hello, I'm Weitao Hou, a PhD student focusing on Geospatial theories and method development at Curtin University. Prior to this, I completed my MSc degree at the National University of Singapore and my BEng at Northwest Normal University.

I describe myself as someone who enjoys exploring the geospatial domain. I aim to innovate new theories and methods for spatial prediction and factor exploration to contribute to sustainable development. While I can't guarantee that these methods will heal the world, I will contribute all my efforts and try to have fun here.

Besides, I used to provide English listening and translation subtitle services for NBA videos, including official content, interviews, and post-game press conferences for players. As a Lakers fan, I must admit translating for Anthony Davis can be quite annoying due to his speed and content.

PS1: I am a whisky nerd, I enjoy most of the Scotch and some of the Burbon. GlenDornoch 18 (BW edition) and Bookers burbon are my favorites!

Research Interests

Geospatial theories

Spatial epidemiology

Sustainable development

Favorite lines

英雄ってのはさ, 英雄になろうとした瞬間に失格なのよ.--仮面ライダー龍騎

Rossi_HOU's Projects

256.py

asdar-book.org

data and scripts for the ASDAR book

awesome-gee-community-datasets

Community Datasets added by users and made available for use at large

bl5323

Cai Xiaotong;Hou Weitao;Jiyeon Yun;Li Dehuan;Zhang Fengqi

data-and-codes-associated-with-vidal-cordasco-et-al.-nature-ecology-evolution-

This repository contains the directories "code" and "data" to reproduce the models, calculations and figure rendering of the paper: Vidal-Cordasco, M; Ocio, D.; Hickler, T., Marín-Arroyo, A.B. (under review) "Spatiotemporal replacement patterns of Neanderthals in Iberia affected by the ecosystems’ productivity". The file 1_Main_Script.R includes the functions to estimate the herbivore biomass of each herbivore species according to the bottom-up processes of the food chain regulation driven by the Net Primary Productivity (NPP), the specific herbivore guild composition in each ecosystem, and the allometric relationships between body mass and population density. This script was also used to analyse and compare the estimated NPP between the four biogeographic regions of Iberia during the MIS 3. Therefore, this code generates the main results and figures as found in the paper. The file 2_HB_Validation.R reproduces the validation process of the macroecological model to estimate herbivore abundances (used in the 1_Main_Script.R) against empirical present-day herbivore densities from a broad range of terrestrial ecosystems. The file 3_NB_NPP.R includes the analyses of the association between NPP and herbivore biomass in different ecosystems and computes a predictive model. The file 4_Pollen.R was used to perform pollen-based paleoclimate reconstructions with weighted averaging (WA) regressions. These predictive functions are used to estimate temperature and precipitation from the palynological fossil record. Finally, these estimations are used to assess the accuracy of a general paleoclimatic circulation model. The file 5_Rarefaction.R performs a rarefaction analysis with the local faunal assemblages in each biogeographic region of Iberia during the MIS 3. As the data and code in this repository are complete, it can be reproduced with only an R environment (tested for R v4.1.0) in RStudio. The necessary package dependencies are documented in each .R file.

ee-palettes

A set of common color palettes for Google Earth Engine

forest-analysis-utilities-r

R codes that are general purpose such as segmentation, pixel value extraction, structural-spectral analysis.

gam-model-for-forcasting_r

Fitting GAM(Generalized Additive Model) to predict Hospital demand data using weather data and other variables as predictors

gcm-filters

Diffusion-based Spatial Filtering of Gridded Data

gcms_scripts

Scripts that help to work with General Circulation Models (GCMs)

gee_study

Gee_Study

google_earth_engine_estimate_aboveground_belowground_biomass

Get the real-time(depending on the temporal resolution of remote sensing data) aboveground biomass and underground biomass

google_earth_engine_long_time_series_analyses

Google Earth Engine Long Time Series Analyses

google_earth_engine_singapore_landuse_nlcd

This is an easy way to use a dataset as the training dataset to do the land use classification, even though that dataset doesn't have the value in your study area.