Using Artificial Intelligence to Calculate Characteristic Town Index

Characteristic town development is a hot topic in China that involves hundreds of billions of dollars of investment. I am extremely excited to announce that using artificial intelligence we have successfully developed Characteristic Town Index for 3,700 towns in China. Based on the index, we are able to identify the most promising towns objectively and efficiently for development. This is the first time that artificial intelligence is used in characteristic town evaluation. It is a cross-team effort under my leadership as the CTO of Hangzhou Jingli Company.

I published an article, Use Big Data and Artificial Intelligence to Rationally Evaluate the Characteristic Town, on influential "Economic Information Daily" (in Chinese) on January 10, 2018 (Please use Google Translate if you are interested). It quickly becomes one of the most cited articles on the Internet in China in this field. After reading the article, a government official of a major city in China says, "Dr. Zhou's article should be read carefully and thoroughly. The points raised are thought-provoking". On one website alone, the article has been read nearly 52 thousand times. Websites hosting the article consistently ranked number one by Google and Baidu on keywords "characteristic town evaluation artificial intelligence" (in Chinese).

The article's main point is that data-driven artificial intelligence models have advantages over experience-based expert systems for decision making. The following are some highlights:

• Multiple teams have spent months to collect 69 variables that are relevant for 3,700 towns. These variables cover climate, geography, economy, ecology, transportation, Internet infrastructure and so on.
• Expert systems are subjective, rigid and static. It is hard to adjust expert systems based on the discrepancies between their outputs and the realities. The feedback mechanism is lacking.
• Data, machine learning models and applications should form closed loops. The data are used to train models. Models' outputs provide decision support for applications. The data are updated and enhanced when models are applied in real world, e.g., finding better target variables. These closed loops allow models to be improved iteratively.

The article has made me instantly famous in this field in China. However, the success doesn't come instantly at all. The core ideas are from my award-winning academic paper published two decades ago "Using genetic learning neural networks for spatial decision making in GIS". I feel fulfilled that I finally implement the ideas for practical applications in the field of GIS.