Developing a Census Based Generative Geodemographic Classification System

开发基于人口普查的生成地理人口分类系统

• 批准号: ES/Z50273X/1
• 负责人: Alex Singleton
• 金额: $ 42.59万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FZ50273X%2F1
• 关键词: Developing; Census; Based; Generative; Geodemographic

Leveraging the power of contemporary Artificial Intelligence (AI), this project aims to revolutionize the way in which we can build and use geodemographic classifications. This will do so by enabling more accurate representations of socio-spatial structure and lowering barriers to census based classification development. It also proposes a user-friendly online tool that will allow anyone to easily create their own tailored, research-ready census-based geodemographic data product.Geodemographic classifications provide useful and policy-relevant representations of the complex and multidimensional characteristics of populations living within small geographic areas. Classifications have been created using components of census data since the 1970s, with notable examples in 2001, 2011 and 2021 when the ONS co-produced the first open geodemographic classifications for the UK with academic partners. These "Output Area Classifications" (OAC) have garnered wide use and inspired localised models for specific geographic areas such as London (LOAC).The core methods used to build geodemographic classification have however remained reasonably static since the 1970s, with only modest update. Furthermore, the creation of classifications also remains a reasonably technical process, limiting the ability for others to produce their own classifications, either for localities or specific purposes.This proposal argues that recent developments in AI, and specifically deep learning and machine learning, show great potential to radically transform the power and utility of geodemographic classification. Firstly, through the creation of more accurate representations of socio-spatial structure; and, secondly, through improved geodemographic information systems that significantly reduce barriers to developing new classificationsAims and ObjectivesThe aim of this project is to update the established methods used to build Census based geodemographic classifications through the integration of AI into:The more automated development of output area level input measures that better account for non-linear geographic relationships between variables.A tool to that enables the automated description of clusters.Enabling the creation of a new public facing and online geodemographic classification system that will enable custom census-based classifications to be created.This will be achieved through the following objectives:Evaluating the use of autoencoders as a new method of data reduction for output area level geodemographic input measures.Developing an operational machine learning pipeline that takes output area level census inputs through to cluster creation.Utilising a large language model (LLM: such as integrated into ChatGPT), to develop an automated geodemographic descriptive tool capable of producing accurate textual descriptions of cluster characteristics.Producing a public facing online tool and accompanying training that will guide users to create their own research-ready census-based geodemographic data products.

该项目利用当代人工智能（AI）的力量（AI），旨在彻底改变我们构建和使用地理人口统计学分类的方式。这样可以通过更准确地表示社会空间结构并降低基于人口普查的分类发展的障碍来做到这一点。它还提出了一种用户友好的在线工具，该工具将使任何人都可以轻松地创建自己量身定制的，基于研究的人口统计学数据产品。地球人口统计学分类提供了生活在小地理区域内的人口的复杂和多维特征的有用且与政策相关的表示。自1970年代以来，已经使用人口普查数据的组件创建了分类，并在2001年，2011年和2021年进行了著名的例子，当时ONS共同制作了英国与学术合作伙伴为英国的第一个开放地球人口统计学分类。这些“输出区域分类”（OAC）已获得广泛的使用，并启发了特定地理区域（例如LOAC）（LOAC）的局部模型。但是，自1970年代以来，用于构建地球人口统计学分类的核心方法一直保持合理的静态状态，只有适度的更新。此外，分类的创建也仍然是一个合理的技术过程，限制了他人出于地方或特定目的而产生自己的分类的能力。该提案认为，AI，特别是深入学习和机器学习的最新发展，具有从根本上改变地理位置分类的力量和实用性。首先，通过创建更准确的社会空间结构表示；其次，通过改进的地球人口统计信息系统，该系统显着降低了开发新的分类和该项目的目标的障碍，是为了更新用于建立基于普查的地理人口统计学分类的既定方法，通过将AI整合到以下方式中：对未经层次的无性地理位置之间的自动化工具的自动化级别的自动化开发。创建新的公共面对面和在线地球人口统计学分类系统，该系统将促进基于自定义的人口普查分类。这将通过以下目标实现：评估使用自动编码器作为一种新的数据减少数据减少数据的方法，用于输出区域级地球人数学级别的地理位车学输入。 （LLM：类似于ChatGpt的集成），以开发一种能够生成集群特征准确的文本描述的自动人口统计学描述工具。生产公开面对的在线工具并伴随培训，以指导用户创建自己的基于研究的研究，基于研究的人口稠密人口统员学数据。