GeoTree

Introducing GeoTree

GeoTree is a computer visualization exploratory analysis tool developed by 201 Spatial Analysis Groupof Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. It uses a "tree" structure to explore the spatiotemporal classification and evolution of phenomena. For spatiotemporal processes with evolution mechanisms, cross-sectional data can be used to reconstruct their classification and temporal evolution processes. The hidden mechanisms and evolutionary changesin multidimensional data are presented in a clear "tree" structure, which can be used for predicting and forecasting regional evolution, and for further understanding the relevant environmental and socio-economic phenomena and their changing trends.

GeoTree v1.0 System

Support Multi-level Model Analysis

The system provides the functionality of multi-level model analysis for two-level branch clustering data. Based on R statistical software, it performs statistical analysis on the first-level branch(representing classification) and secondary branch (representing evolutionary stages) and displays the statistical results.

Easy Geodetector q Statistical Analysis

Geodetector q statistical analysis analyzes the explanatory power of a given factor X on the spatial differentiation of research object Y:

q=( 1- h=1 LNh σh2 Nσ2 )×100%

where h = 1, ..., L represents the strata of variable Y or factor X, such as classification, zoning, grouping, or some other type of partitioning. N_h and N are the number of units in stratum h andthe total population, respectively. σ_h^2 and σ^2 are the variances of Y in stratum h and the total population, respectively. The value of q ranges from 0 to 1. For more information on the Geodetector q statistical analysis, please visit www.geodetector.cn.

Multiple Cluster Methods are Provided

The clustering methods included in the system are Simple K-means, EM, Filtered, Hierarchical, Farthest First and Wards. In addition, the secondary branch clustering module provides a method of clustering based on the level of social development of the study unit.

Construct your Evolutionary Tree

A tree model for the results of clustering analysis, showing the evolutionary patterns of research objects in a hierarchical tree form, and supports linkage display with geographic data.

Visualization of Markov Chain

Markov Chain is used to analyze the transition between different states.

Download

Unzip this file into the place where you want to install it. This can be anywhere, for example, your Program Files directory. GeoTree is built with Java dependencies, you can download the jdk here.

Download Lateast v1.0

Document

The detailed document of our software is here.

Example cases

Case 1: Geography evolution of Chinese cities

This case study uses the Geotree method to explore the relationship between land expansion of Chinese cities and their types and development stages. The primary branch represents city types, and the secondary branch represents the development stages of cities (Jinfeng Wang et al., 2012). For more details, please refer to Chapter 19 of the 2nd edition of the Spatial Data Analysis Tutorial (《空间数据分析教程》).

Case 2: Global Geotree of non-communicable disease incidence rate

In this case, a global non-communicable disease evolution tree was constructed based on socio-economic types and development stages (Yang Wang et al., 2020). The primary branch is classification of country types and the secondary branch the classification of country development stages.

Case 3: Tree-like evolution of global urban land expansion

This case combines socioeconomic data from 162 countries in 2005 and 2015 and uses the Geotree model to explore the relationship between global urban land expansion and country type and development stage. The primary branch is the type of industrial structure, and the secondary branch is the development stage of the country.

Reference

2012 Wang JF, Liu XH, Peng L, Chen HY, Driskell L, Zheng XY, 2012. Cities evolution tree and applications to predicting urban growth. Population and Environment, 33(2): 186-201.

2020 Wang Y, Wang JF, 2020. Modelling and prediction of global non-communicable diseases. BMC Public Health, 20(1): 822.

2022 Jing SQ, Wang JF, Xu CD, Yang JT, 2022. Tree-like evolution pathways of global urban land expansion. Journal of Cleaner Production, 378: 134562.

2023 Lei YH, Wang JF, Wang Y, Xu CD. 2023. Geographical evolutionary pathway of global tuberculosis incidence trends. BMC Public Health, 23:755

Contact

For any questions, please contact us .