From f21e3b2e0b98996823612bb2be678df9da56baf0 Mon Sep 17 00:00:00 2001 From: chenyangkang Date: Tue, 29 Aug 2023 16:44:23 +0800 Subject: [PATCH] Aug29 --- README.md | 2 +- docs/index.md | 2 +- setup.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 1d0ce9f..8acec5e 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ pip install BirdSTEM ``` ## Brief introduction -Bird STEM is an AdaSTEM model for daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding bird observation in space and time, to predict the occurence and abundance of target spatial-temporal point. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurence modeling and XGBoostRegressor for abundance modeling. +BirdSTEM is an AdaSTEM model for daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding bird observation in space and time, to predict the occurence and abundance of target spatial-temporal point. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurence modeling and XGBoostRegressor for abundance modeling. User can define the size of stixel (spatial temporal pixel) in terms of space and time. Larger stixel guarantees generalizability but loses precision in fine resolution; Smaller stixel may have better predictability in the exact area but reduced extrapolability for points outside the stixel. diff --git a/docs/index.md b/docs/index.md index b90278c..be70e59 100644 --- a/docs/index.md +++ b/docs/index.md @@ -12,7 +12,7 @@ pip install BirdSTEM Daily Abundance & phenology estimation using eBird citizen science data ## Brief introduction -Bird STEM is an AdaSTEM model for daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding bird observation in space and time, to predict the occurence and abundance of target spatial-temporal point. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurence modeling and XGBoostRegressor for abundance modeling. +BirdSTEM is an AdaSTEM model for daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding bird observation in space and time, to predict the occurence and abundance of target spatial-temporal point. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurence modeling and XGBoostRegressor for abundance modeling. User can define the size of stixel (spatial temporal pixel) in terms of space and time. Larger stixel guarantees generalizability but loses precision in fine resolution; Smaller stixel may have better predictability in the exact area but reduced extrapolability for points outside the stixel. diff --git a/setup.py b/setup.py index ea5afbf..dbcde8e 100755 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh: long_description = "\n" + fh.read() -VERSION = '0.0.2' +VERSION = '0.0.3' DESCRIPTION = 'AdaSTEM model for daily abundance estimation using eBird citizen science data' LONG_DESCRIPTION = 'TBD'