High levels of survey nonresponse potentially produce unreliable data due to the often indeterminable possibility of such data being subject to nonresponse bias. In this paper, spatial patterns of global nonresponse rate are analyzed in order to identify whether systemic bias exists across urban spaces with regard to survey nonresponse. Forward stepwise regression is used in combination with spatial regression analysis to build models enabling the prediction of global nonresponse rates in the voluntary 2011 National Household Survey based on explanatory employment, housing, income, and other variables within 11 Canadian cities. The modelling process underscores the inequity of global nonresponse rates; places with high unemployment, high rates of rental properties, a higher proportion of Aboriginal residents, and lower educational attainment have lower compliance with the voluntary survey. Such a pattern has the potential to dramatically influence the ability of government, non‐governmental organizations, and other service providers to address the needs of residents of such urban areas.

中文翻译：

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绘制城市中不确定数据的空间格局：劣势人群在全国家庭调查中预测全球无响应率

高级别的调查无响应可能会产生不可靠的数据，这是因为此类数据经常会出现无响应偏差的不确定性。在本文中，分析了全球无响应率的空间格局，以便确定关于调查无响应的整个城市空间是否存在系统性偏差。前向逐步回归与空间回归分析结合使用，可建立模型，从而根据加拿大11个城市的解释性就业，住房，收入和其他变量，对2011年全国家庭自愿调查中的全球无应答率进行预测。建模过程强调了全球无应答率的不平等；高失业率，高出租物业，原住民比例较高的地方，受教育程度较低的人对自愿调查的依从性较低。这种模式有可能极大地影响政府，非政府组织和其他服务提供商解决此类城市居民需求的能力。