Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850–1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20–50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider the quantification methods that are the most logical for the geographic context and the type of analyses.

中文翻译：

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地理编码级别在历史人口统计分析中的重要性：以1850–1914年瑞典乡村教区为例

对纵向和个人级别的历史人口统计数据库进行地理编码，可以对微观级别的地理因素如何长期影响人口统计结果进行新颖的分析。然而，这种详细的地理编码涉及高成本。此外，如果使用不合适的方法来量化地理因素，则无法正确利用高空间分辨率。我们评估了不同的地理编码级别和用于定义地理变量的方法如何影响详细的空间和历史人口统计分析的结果。使用在地物单位级别进行地理编码的纵向和个人级别的人口统计数据库，我们分析了人口密度和邻近湿地对1850–1914年居住在五个瑞典教区中的个人的全因死亡率的影响。我们使用两种常见的量化方法对每个地理变量在三个详细的地理编码级别上比较分析结果。连同选择的用于量化地理因素的方法一起，即使属性单元之间的位置精度（20-50 m）之间的微小差异，以及稍微粗糙的地理水平也严重影响了人口统计分析的结果。结果还显示了考虑随时间变化的地理区域的重要性。最后，靠近湿地和人口密度分别影响了妇女和儿童的死亡率。但是，所有可能的死亡率决定因素均未在分析中进行评估。总之，对于农村历史地区，对几百米范围内的距离进行精细规模分析可能需要对属性单位进行地理编码。