{"title":"地理集中度指数与数据聚合的检验","authors":"E. Auvray, Salima Bouayad-Agha","doi":"10.3917/ecop.216.0002","DOIUrl":null,"url":null,"abstract":"To characterise the spatial concentration of economic activities, reliable statistical measures are needed. This allows assessment of existing disparities and comparison of concentration levels by sector in time and space. Space is continuous but its discretisation due to spatial grouping of observations at different geographical scales (municipalities, d?partements, regions) can induce a measurement error (Briant et alii, 2010), thus affecting the representation of the concentration. Since it is not always possible to utilise the exact position of the entities, this work proposes to study, from simulated data, the extent to which the most commonly used indices of geographic concentration of activities can be biased by geographical aggregation. We show that index values are sensitive to the geographical scale on which they are calculated and that some indices are more robust than others to geographic aggregation.","PeriodicalId":141680,"journal":{"name":"Économie & prévision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Les indices de concentration géographique à l’épreuve de l’agrégation des données\",\"authors\":\"E. Auvray, Salima Bouayad-Agha\",\"doi\":\"10.3917/ecop.216.0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To characterise the spatial concentration of economic activities, reliable statistical measures are needed. This allows assessment of existing disparities and comparison of concentration levels by sector in time and space. Space is continuous but its discretisation due to spatial grouping of observations at different geographical scales (municipalities, d?partements, regions) can induce a measurement error (Briant et alii, 2010), thus affecting the representation of the concentration. Since it is not always possible to utilise the exact position of the entities, this work proposes to study, from simulated data, the extent to which the most commonly used indices of geographic concentration of activities can be biased by geographical aggregation. We show that index values are sensitive to the geographical scale on which they are calculated and that some indices are more robust than others to geographic aggregation.\",\"PeriodicalId\":141680,\"journal\":{\"name\":\"Économie & prévision\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Économie & prévision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3917/ecop.216.0002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Économie & prévision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3917/ecop.216.0002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
为了描述经济活动的空间集中,需要可靠的统计措施。这样就可以在时间和空间上评估现有的差距和按部门比较集中程度。空间是连续的,但由于不同地理尺度(直辖市,d?部分,地区)会引起测量误差(Briant et alii, 2010),从而影响浓度的表示。由于不可能总是利用实体的确切位置,因此这项工作建议从模拟数据中研究最常用的地理活动集中指数在多大程度上可能受到地理聚集的影响。我们发现指标值对其计算的地理尺度敏感,并且一些指数对地理聚集的鲁棒性比其他指数更强。
Les indices de concentration géographique à l’épreuve de l’agrégation des données
To characterise the spatial concentration of economic activities, reliable statistical measures are needed. This allows assessment of existing disparities and comparison of concentration levels by sector in time and space. Space is continuous but its discretisation due to spatial grouping of observations at different geographical scales (municipalities, d?partements, regions) can induce a measurement error (Briant et alii, 2010), thus affecting the representation of the concentration. Since it is not always possible to utilise the exact position of the entities, this work proposes to study, from simulated data, the extent to which the most commonly used indices of geographic concentration of activities can be biased by geographical aggregation. We show that index values are sensitive to the geographical scale on which they are calculated and that some indices are more robust than others to geographic aggregation.