基于多源时空大数据的区际迁徙人群多层次空间分布估算模型——以COVID-19疫情期间自武汉迁出人群为例

已有研究很少关注区际迁徙人群在不同尺度上空间分布的动态估算问题。COVID-19疫情爆发以来,坚决防止疫情扩散成为社会最紧迫的事情。在2020年1月23日武汉“封城”前夕,已有500多 万人离开了武汉,快速准确地推算这部分人群的去向,可以为防止疫情扩散和制定防疫决策提供科学依据。本文以此为例,基于开源腾讯位置请求大数据、百度迁徙大数据、土地覆盖数据等多源地理时空大数据,提出一种区际迁徙人群多层次空间分布动态估算模型,用于推算2020年除夕 （2020年1月24日）之前从武汉流入湖北省内各地的人群数量及其分布特征。结果显示：① 春节时段湖北省各地级市农村地 区人群增加数量占人群变化总量的比例平均达124.7%,从武汉市迁入各地级市的人群中至少51.3%流入农村地区;② 区县尺 度人群变化总量的空间分布呈现3个圈层结构：第一圈层为疫情核心区,包括武汉及其周边地区,以人群流出为主;第二圈层为 重点关注区,包括黄冈、黄石、仙桃、天门、潜江、随州、襄阳,以及孝感、荆门、荆州和咸宁的部分地区,以人群总量和农村地区人 群数量大幅增加为主;第三圈层为次级关注区,包括湖北西部宜昌、恩施、神农架和荆门部分地区,以人群小幅流入为主。最后,建议湖北省内,尤其是位于第二圈层内的区县,应高度关注农村地区人群的疫情防控。此研究成果在2~3天完成,显示大数据是可以快速地响应重大公共安全事件,为决策的制定提供一定支持的。     

土地利用变化预测CBR方法的适应性分析

研究土地利用变化的方法很多,常见的有统计回归方法、概率统计方法、系统动力学方法、元胞自动机方法以及贝叶斯网络方法等。这些方法都各具特点,并得到了实际应用。虽然有些研究指出了一些方法的优点和不足,但是对其在土地利用变化问题上的适应性研究还比较有限。案例推理（Case Based Reasoning,简称CBR）作为研究土地利用变化的一种新方法,目前同样缺乏适应性方面的研究。因此,本文在土地利用的CBR方法的研究基础上,具体探讨该方法在土地利用变化预测上的适应性问题。研究分别从案例的选取方式、模型指标的选取以及指标权重的设定3个方面开展,通过对比试验以深入讨论3个方面的因素对预测精度的影响。研究结果显示,在历史案例丰富的情况下,通过选择重要的指标,并对其赋予较高权重,可以保证CBR方法在预测土地利用变化时的稳定性。此外,CBR中＂地理环境＂组分的引入,有效地提高了土地利用变化预测的精度。研究表明,CBR在解决土地利用变化问题上具有简单灵活、适用范围广、预测精度高以及保持形态稳定的特点,是一种解决地学问题的新方法。     

东莞地区土地利用变化预测的CBR和CA方法对比研究(英文)

Many studies on land use change(LUC),using different approaches and models,have yielded good results.Applications of these methods have revealed both advantages and limitations.However,LUC is a complex problem due to influences of many factors,and variations in policy and natural conditions.Hence,the characteristics and regional suitability of different methods require further research,and comparison of typical approaches is re-quired.Since the late 1980s,CA has been used to simulate urban growth,urban sprawl and land use evolution successfully.Nowadays it is very popular in resolving the LUC estimating problem.Case-based reasoning(CBR),as an artificial intelligence technology,has also been employed to study LUC by some researchers since the 2000s.More and more researchers used the CBR method in the study of LUC.The CA approach is a mathematical system con-structed from many typical simple components,which together are capable of simulating complex behavior,while CBR is a problem-oriented analysis method to solve geographic problems,particularly when the driving mechanisms of geographic processes are not yet understood fully.These two methods were completely different in the LUC research.Thus,in this paper,based on the enhanced CBR model,which is proposed in our previous research(Du et al.2009),a comparison between the CBR and CA approaches to assessing LUC is presented.LUC in Dongguan coastal region,China is investigated.Applications of the im-proved CBR and the cellular automata(CA) to the study area,produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR,and 70.7% accuracy from the CA.From the results,we can see that the accuracies of the CA and CBR approaches are both >70%.Although CA method has the distinct advantage in predicting the urban type,CBR method has the obvious tendency in predicting non-urban type.Considering the entire ana-lytical process,the preprocessing workload in CBR is less than that of the CA approach.As such,it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.