In the Zip, spectral. npy was the average spectral data of red ginseng, mycotoxins and interference impurities, and label. npy was the corresponding label. Spectral data format was [1200,510] and label data format was [1200,1]. The example of data usage (sklearn in Python database was used to establish the classification model) was as follows:

import numpy as np
from sklearn. model_selection import train_test_split
from sklearn. preprocessing import StandardScaler
from sklearn. neighbors import KNeighborsClassifier
from sklearn. metrics import classification_report, accuracy_score

# Load spectral data and labels
x = np.load('.../spectral.npy')[:,1:-1]
y = np.load('.../label.npy')

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Data standardization
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)

# Train the KNN model
knn_model = KNeighborsClassifier(n_neighbors=5)
knn_model. fit(x_train, y_train)

# Predict
y_pred = knn_model.predict(x_test)

# Print classification reports and accuracy rates
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Accuracy Score:")
print(accuracy_score(y_test, y_pred))

The compressed package (Study_code.zip) contains the code files implemented by an under review paper ("What you see is what you get: Delineating urban jobs-housing spatial distribution at a parcel scale by using street view imagery based on deep learning technique").The compressed package (input_land_parcel_with_attributes.zip) is the sampled mixed "jobs-housing" attributes data of the study area with multiple probability attributes (Only working, Only living, working and living) at the land parcel scale.The compressed package (input_street_view_images.zip) is the surrounding street view data near sampled land parcels (input_land_parcel_with_attributes.zip) with the pixel size of 240*160 obtained from Tencent map (https://map.qq.com/).The compressed package (output_results.zip) contains the result vector files (Jobs-housing pattern distribution and error distribution) and file description (Readme.txt).This project uses some Python open source libraries (Numpy, Pandas, Selenium, Gdal, Pytorch and sklearn). This project complies with the GPL license.Numpy (https://numpy.org/) is an open source numerical calculation tool developed by Travis Oliphant. Used in this project for matrix operation. This library complies with the BSD license.Pandas (https://pandas.pydata.org/) is an open source library, providing high-performance, easy-to-use data structures and data analysis tools. This library complies with the BSD license.Selenium(https://www.selenium.dev/) is a suite of tools for automating web browsers.Used in this project for getting street view images.This library complies with the BSD license.Gdal(https://gdal.org/) is a translator library for raster and vector geospatial data formats.Used in this project for processing geospatial data.This library complies with the BSD license.Pytorch(https://pytorch.org/) is an open source machine learning framework that accelerates the path from research prototyping to production deployment.Used in this project for deep learning.This library complies with the BSD license.sklearn(https://scikit-learn.org/) is an open source machine learning tool for python.Used in this project for comparing precision metrics.This library complies with the BSD license.

The dataset consists of particulate matter concentration and meteorology data, measured in Singapore, Chinatown, and Central business district from March 13, 2018, to March 16, 2018. The data collectors walked from the Outram district - Chinatown to the Central Business District in Singapore. The measurements were carried out using a hand-held air quality sensor ensemble (URBMOBI 3.0).

The dataset contains information from two URBMOBI 3.0 devices and one reference-grade device (Grimm 1.109). The data from the sensors and Grimm are denoted by the subscript, 's1', 's2', and 'gr', respectively.

singapore_all_pm_25.geojson : The observed PM concentration and meteorology, aggregated using a 25 m buffer around the measurement points.

Information on working with geojson file can be found under GeoJSON .

Units:
PM : µg/m³
Scaled_PM_MM : Dimensionless entity scaled using Min-Max-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
Scaled_PM_SS : Dimensionless entity scaled using Standard-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
Air temperature: °C
Relative humidity: %

The measurements are part of the "Effects of heavy precipitation events on near-surface climate and particulate matter concentrations in Singapore". It is funded by the support from Humboldt-Universität zu Berlin for seed funding for collaborative projects between National University of Singapore and Humboldt-Universität zu Berlin.

Overview

https://i.imgur.com/ZUX61cD.png" alt="Overview">

Context

The method of disuniting similar data is called clustering. you can create dummy data for classifying clusters by method from sklearn package but it needs to put your effort into job.

For users who making hard test cases for example of clustering, I think this dataset helps them.

Try out to select a meaningful number of clusters, and dividing the data into clusters. Here are exercises for you.

Dataset

All csv files contain a lots of x, y and color, and you can see above figures.

If you want to use position as type of integer, scale it and round off to integer as like x = round(x * 100).

Furthermore, here is GUI Tool to generate 2D points for clustering. you can make your dataset with this tool. https://www.joonas.io/cluster-paint

Stay tuned for further updates! also if any idea, you can comment me.