Research methods in social science take advantage from broader trends such as digitalization and increasing computational power, however, this is an evolving explorative search. The main purpose of this article is to describe the methodological innovations in the collection and processing of geo-referenced imagery for the observation of neighborhood disorder. In this narrative review, attention is paid to advances in both the data sources and the data processing methods used. Neighborhood disorder is traditionally measured by means of survey methods and (systematic) (social) observations, but these methods have specific shortcomings, such as respectively the subjective measurement that does not deliver a valid measure of actual prevalence of disorderly phenomena and the intensive use of resources in terms of time and money. This has repercussions for (the interpretation of) the results based on these data. Today, scholars have innovative data sources and cutting-edge data processing methods at their disposal that can meet (some of) these shortcomings, but which have not yet been fully explored. In this article, the evolutions in the use of geo-referenced imagery for the observation of neighborhood disorder from the last 25 years are described with a focus on the empirical opportunities, and the methodological challenges and prospects. We conclude by outlining the road ahead: promising avenues for future research to exploit the full potential of ‘big primary data’.

社会科学的研究方法利用了更广泛的趋势，例如数字化和计算能力的增强，但是，这是一种不断发展的探索性搜索。本文的主要目的是描述用于观察邻域混乱的地理参考影像的收集和处理的方法论创新。在此叙述性审查中，关注数据来源和所用数据处理方法的进步。邻里障碍传统上是通过调查方法和（系统的）（社会）观察来衡量的，但是这些方法具有特定的缺点，例如主观测量无法有效衡量无序现象的实际发生率以及大量使用时间和金钱方面的资源。这会对基于这些数据的结果（解释）产生影响。今天，学者们拥有创新的数据来源和前沿的数据处理方法，可以弥补这些（部分）不足，但尚未得到充分探索。在本文中，描述了过去 25 年来使用地理参考图像观察邻里混乱的演变，重点介绍了经验机会、方法论挑战和前景。最后，我们概述了未来的道路：未来研究的有希望的途径，以利用“大原始数据”的全部潜力。但尚未完全探索。在本文中，描述了过去 25 年来使用地理参考图像观察邻里混乱的演变，重点介绍了经验机会、方法论挑战和前景。最后，我们概述了未来的道路：未来研究的有希望的途径，以利用“大原始数据”的全部潜力。但尚未完全探索。在本文中，描述了过去 25 年来使用地理参考图像观察邻里混乱的演变，重点介绍了经验机会、方法论挑战和前景。最后，我们概述了未来的道路：未来研究的有希望的途径，以利用“大原始数据”的全部潜力。