越大越好:为什么小区域的地理位置是最好的可操作指数开发
William R Buckingham1, W Ryan Powell1,2, Sarah Anne Keller1
1Center for Health Disparities Research, University of Wisconsin-Madison.
概括
使用较小的地理水平为区域贫困指数 (ADI) 更好地识别健康差异. 处境不利的社区在人口普查区组级别上显示出更高的再录取率,但不是在更大的规模上.
科学领域:
- 公共卫生 公共卫生
- 健康差异研究 研究健康差异研究
- 地理空间分析是什么?
背景情况:
- 区域层面的社会影响力测量对于了解健康差异至关重要.
- 可修改面积单位问题 (MAUP) 可以影响这些措施的效用.
- 较小的地理单位可以为卫生政策的调整提供更高的测量精度.
研究的目的:
- 为了评估区域级的劣势和30天的再入院之间的关联.
- 评估地理规模对区域贫困指数 (ADI) 的影响及其与再接收的关系.
- 为了说明地理分辨率在健康差异研究中的重要性.
主要方法:
- 使用的医疗保险100%服务费住院索赔数据.
- 在包括人口普查区组在内的各种地理层面计算了区域贫困指数 (ADI).
- 在最弱势地区和最弱势地区之间的30天再接收率进行比较.
主要成果:
- 在最不利的人口普查区组中,与最不不利的人口普查区组相比,在30天内再入院的几率提高了20%.
- 当ADI在更大的地理层面上总结时,这种关联并不显着.
- 较小的地理分辨率显示,处于不利地位和再接收之间存在更强的关联.
结论:
- 较小的地理层面,例如人口普查区组,是捕捉区域层面的不利影响对健康结果 (如再入院) 的最佳方式.
- 针对健康差异的政策干预措施在使用细粒度空间数据时可能会更有效地协调.
- 为了公众的可用性和有效的卫生政策,必须更加关注提供小区域健康数据.
相关概念视频
Manipulation and Analysis
23
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
23
Selected Data About Geographic Locations
27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Levels of Use of a GIS
46
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
46
Thematic Layering in GIS
35
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
35
Introduction to GIS
60
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
60
Sampling Plans
170
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
170


