Applying Remote Sensing and Geographic Information System to Arab Fertility

这项研究通过调查两个阿拉伯国家：埃及和约旦的城市和农村环境中生育率差异和随时间变化的空间模式的存在，加深了对阿拉伯生育率转变的理解。这项研究是在一个概念框架的指导下进行的，该框架将生育率转变解释为当地环境中人力资本的变化(供需框架)和关于家庭规模的思想和行为的空间扩散(文化扩散视角的“横向”部分)。这项研究的主要目的是探索阿拉伯国家农村和城市地区生育率变化的时空成分，其基础是更普遍的假设，即生育行为是你是谁和你在哪里的函数。该项目扩展了研究人员已经开始的工作，在阿拉伯生育率转变的分析中加入了明确的空间成分。这一空间部分有两个重要方面：(1)衡量你所处的地方对生殖行为的影响程度，而不是你是谁；(2)量化做出生殖决定的环境背景。这些目标将通过应用遥感技术、地理信息系统(地理信息系统)和当地空间关联指标，并与埃及和约旦研究地点的人口普查数据相结合来实现。卫星图像提供了生成关于发生生殖行为的当地地区的生态/环境背景的信息的能力，否则无法获得这些信息。将这些变量纳入具有普查数据的地理信息系统，提供了一种使用新兴的空间统计技术对信息进行统计分析的方法。这些技术可以对低生育率和高生育率的空间集群性进行定量评估，还可以计算空间过滤回归模型，这些模型能够区分因变量(生育率)的变异性是由空间成分(你在哪里)引起的，还是由非空间成分(你是谁)引起的。尽管该项目没有单独率先使用这些技术中的任何一种，但还没有人口学家以这种方式将所有这些片段组合在一起。由于这些技术在人口学研究中仍然是相当新的技术，本研究项目的实施和产品将为促进空间视角和方法在人口学教育和研究中的作用提供模式。该项目将表明，要了解区域生育率的转变，就需要了解生育率水平及其随时间的变化如何表现出空间集聚，以及空间集聚与特定社会生态环境的关系。所采用的方法提供了一个模型，说明增加地方预测可以如何提高可能影响生殖决策的地方适用政策的有效性。在更广泛的社会层面上，实质性成果将有助于指导明智地利用始终稀缺的资源来改善发展中国家的生殖健康。

This research adds to an understanding of the Arab fertility transition by investigating the existence of spatial patterns of fertility differentials and change over time in urban and rural settings in two Arab nations: Egypt and Jordan. The research is guided by a conceptual framework that explains the fertility transition as a combination of human capital changes in local contexts (the supply-demand framework) and the spatial diffusion of ideas and behavior regarding family size (the "horizontal" component of the cultural diffusion perspective). The major thrust of the research is oriented toward the exploration of the spatio-temporal component of fertility change in rural and urban areas in Arab countries, predicated on the more general hypothesis that reproductive behavior is a function of both who you are and where you are. The project extends the work that the researchers have already begun incorporating an explicitly spatial component to the analysis of the Arab fertility transition. This spatial component has two important aspects: (1) measuring the extent to which where you are influences reproductive behavior, net of who you are; and (2) quantifying the environmental context in which reproductive decisions are being made. These objectives will be accomplished by applying techniques of remote-sensing, geographic information system (GIS), and local indicators of spatial association and combining them with census data for study sites in Egypt and Jordan. The satellite imagery offers the ability to generate otherwise unavailable information about the ecological/environmental context of the local areas in which reproductive behavior is occurring. The incorporation of these variables into a GIS with the census data offers a way to statistically analyze the information using emerging spatial-statistical techniques. These techniques permit the quantitative assessment of spatial clustering of low and high fertility, and they also permit the calculation of spatially filtered regression models which are able to distinguish between variability in the dependent variable (fertility) that is due to the spatial component (where you are) and that which is due to the non-spatial component (who you are). Although the project is not individually pioneering the use of any of these techniques, no demographers have yet put all of these pieces together in this way. Since these techniques are still quite new in demographic research, the conduct of and products from this research project will provide models for advancing the role of spatial perspectives and methods in demographic education and research. The project will demonstrate that an understanding of regional fertility transitions requires an understanding of the way in which fertility levels and their change over time exhibit spatial clustering, and of the way in which spatial clustering is related to specific social ecological environments. The methods employed offer a model of how an increase in local prediction could increase the effectiveness of locally applied policies that may influence reproductive decisions. At the broader societal level, the substantive results will help to guide intelligent use of always-scarce resources in improving reproductive health in developing countries.