Getis'G

Distinguish between hot and cold spots

Local Spatial autocorreclation

true

Contributor(s)

Initial contribute: 2021-06-16

Classification(s)

Method-focused categoriesData-perspectiveGeostatistical analysis

Detailed Description

English {{currentDetailLanguage}} English

Moran's I and Geary's C have good statistical characteristics to describe global spatial autocorrelation, but they do not have the ability to identify different types of spatial aggregation patterns, such as cold spots and hot spots.

General G:

The General G statistic of overall spatial association is given as

\( G=\frac{\sum_{i=1}^{n}w_{ij}x_ix_j}{\sum_{i=1}^{n}x_ix_j} \)

where \( x_i \) and \( x_j \) are observations for features i and j, and \( w_{ij} \) is the spatial weight between feature i and j. n is the number of elements and \( x_i \) couldn’ t be the same as \( x_j \).

Meanwhile the expection of General G is E(G), \( E(G)=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}}{n(n-1)} \).So When the General G value is higher than the observed E(G) value, there is a high value aggregation.When the General G value is lower than the observed value of E(G), there is address aggregation.As Genera G approaches E(G) the observed values are randomly distributed in space.

Local G:

The local G-exponent can be expressed as

\( G_i^*=\frac{\sum_{j=1}^nw_{ij}x_j-x_{average}\sum_{j=1}^nw_{ij}}{s\sqrt{\frac{[n\sum_{j=1}^nw_{ij}^2-(\sum_{j=1}^nw_{ij})^2]}{n-1}}} \)

where \( x_p=\frac{\sum_{j=1}^{n}x_j}{n} \) and \( s=\sqrt{\frac{\sum_{j=1}^{n}x_j^2}{n}-x_{average}^2} \).This index can be used to identify spatial clusters of high and low values (hot spots) of statistical significance and to tell us where the high and low values are clustered.

模型元数据

{{htmlJSON.HowtoCite}}

{{htmlJSON.Copy}}

Contributor(s)

Initial contribute : 2021-06-16

{{htmlJSON.CoContributor}}

History

Last modifier
wu kai
Last modify time
2021-06-18
Modify times
View History

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









{{htmlJSON.RelatedItems}}

{{htmlJSON.LinkResourceFromRepositoryOrCreate}}{{htmlJSON.create}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

{{htmlJSON.authorshipSubmitted}}

Cancel Submit
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Localizations}} + {{htmlJSON.Add}}
{{ item.label }} {{ item.value }}
{{htmlJSON.ModelName}}:
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
名称 别名 {{tag}} +
系列名 版本号 目的 修改内容 创建/修改日期 作者
摘要 详细描述
{{tag}} + 添加关键字
* 时间参考系
* 空间参考系类型 * 空间参考系名称

起始日期 终止日期 进展 开发者
* 是否开源 * 访问方式 * 使用方式 开源协议 * 传输方式 * 获取地址 * 发布日期 * 发布者



编号 目的 修改内容 创建/修改日期 作者





时间分辨率 时间尺度 时间步长 时间范围 空间维度 格网类型 空间分辨率 空间尺度 空间范围
{{tag}} +
* 类型
图例


* 名称 * 描述
示例描述 * 名称 * 类型 * 值/链接 上传


{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Add}} {{htmlJSON.Cancel}}

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
{{htmlJSON.Cancel}} {{htmlJSON.Confirm}}