Developing a Census Based Generative Geodemographic Classification System

利用当代人工智能（AI）的力量，该项目旨在彻底改变我们构建和使用地理人口分类的方式。这将通过更准确地表示社会空间结构和降低基于普查的分类发展的障碍来实现。它还提出了一个方便用户的在线工具，使任何人都能方便地创建适合自己的、可用于研究的人口普查地理人口数据产品，地理人口分类提供了有用的、与政策相关的资料，说明生活在小地理区域内的人口的复杂和多层面特征。自20世纪70年代以来，已经使用人口普查数据的组成部分创建了分类，其中值得注意的例子是2001年，2011年和2021年，当时国家统计局与学术合作伙伴共同为英国制作了第一个开放的地理人口分类。这些“输出地区分类”（OAC）已经获得了广泛的使用，并为特定的地理区域，如伦敦（LOAC）的本地化模型的灵感。然而，用于建立地理人口分类的核心方法自20世纪70年代以来一直保持相当静态，只有适度的更新。此外，创建分类仍然是一个合理的技术过程，限制了其他人为地方或特定目的创建自己的分类的能力。该提案认为，人工智能的最新发展，特别是深度学习和机器学习，显示出巨大的潜力，可以从根本上改变地理人口分类的力量和效用。首先，通过创建更准确的社会空间结构表示;其次，通过改进地理人口信息系统，大大减少开发新分类的障碍。更自动化地开发产出领域水平的投入措施，更好地说明非变量之间的线性地理关系。一个工具，使集群的自动化描述。使创建一个新的面向公众和在线地理人口分类系统，将使自定义人口普查-这将通过以下目标实现：评估自动编码器的使用，作为输出区域级地理人口输入措施的数据减少的新方法。开发一个可操作的机器学习管道，将输出区域级人口普查输入用于集群创建。利用大型语言模型（LLM：例如集成到ChatGPT中），开发一个自动化的地理人口描述工具，能够对聚类特征进行准确的文字描述。开发一个面向公众的在线工具，并提供相关培训，指导用户创建自己的研究用普查地理人口数据产品。

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.