whygist,github

abmgis

Accompanying resources for the book 'Agent-Based Modelling and Geographical Information Systems: A Practical Primer'.

bert_classification

利用bert预训练的中文模型进行文本分类数据集中文情感分析语料chnsenticorp

chinese-bert-wwm

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

coupled_abm-flood_model_stmaarten

对应文献：A coupled flood-agent-institution modelling (CLAIM) framework for urban flood risk management

crawlerproject

爬虫项目：链家网（普通/scrapy）、虎扑、维基百科、百度地图api、房天下（分布式爬虫）、微信公众号（代理池爬取）

meituanappspider

美团APP爬虫，可获取指定城市范围内所有美食店铺信息，包含店铺名称、类别、评分、所属片区、经纬度、详细地址、优惠套餐情况、营业时间、联系电话、累计售出份数、餐厅简介、特色菜......

mesacat

Mesa Model of Flood Evacuation using OpenStreetMap

sentiment-analysis-topic-modeling-for-hotel-reviews

Web Scraping, Sentiment Analysi, Latent Dirichlet Allocation (LDA) topic modelling

Climate change intensifies the frequency and severity of floods - the most devastating and costly climate-induced hazard. Government adaptations such as dikes or beach nourishments are important, yet insufficient in the face of worsening hazards. They reduce the hazard probability, but the local actions at the household level determine the extent of damages and inequalities in its distributional impacts on various societal groups. To design effective policies and risk reduction strategies it is critical to understand the factors that motivate household adaptive actions. Quantifying the cumulative impacts of these factors on in terms of the speed and scope of household adaptation enables insights into the adaptation behaviour and risk exposure of different social groups. This fosters the design of multi-actor cross-scale flood adaptation strategies which reduce aggregate risks and address “within-country inequalities” of climate change. This paper presents a spatially explicit agent-based model for the case of Shanghai, China to understand the impacts of household climate change adaptation to coastal floods and to draw new insights for policymakers. Specifically, we combine geolocation data of residential buildings with inundation data under various climate change scenarios to model the exposure of households in Shanghai. We further parameterize the households using context-specific micro-level survey data from Shanghai and depict household adaptation decisions using an extended version of the Protection Motivation Theory.