The geographies of perceived neighbourhood disorder. A small area estimation approach

Buil‐Gil, David; Medina, Juanjo; Shlomo, Natalie

doi:10.1016/j.apgeog.2019.102037

Cited by 11 publications

(3 citation statements)

References 36 publications

(69 reference statements)

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“…For example, Park et al [53] show that the urbanization indicator of the Inner Mongolia region is strongly and significantly influenced by the dimension of economic development, while the Mongolia region is strongly and significantly influenced by the dimensions of social goods and economic development. The literature also brings other examples of works that use PLS-SEM to explore how the dimensions of a phenomenon are related and influence indicators of quality of work [54], fair and sustainable well-being [55], disorder perceived in the neighborhood [56], and social cohesion [31].…”

Section: Structural Equation Modeling (Sem)mentioning

confidence: 99%

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted—or not—as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and consequently distort the assessment of intra-urban inequality. In this paper, we explore the direct and indirect effects that the different underlying dimensions of intra-urban inequality have on indicators that represent this phenomenon. Structural equation modeling was used to build a composite indicator that captures the direct and indirect effects of the underlying dimensions of intra-urban inequality. From this modeling that combines confirmatory factor analysis with a system of simultaneous equations, the intra-urban inequality of the urban conurbation of Maringá–Sarandi–Paiçandu, Brazil was measured. The model comprises first- and second-order structures. The first-order structure is composed of non-observed variables that represent three underlying dimensions of intra-urban inequality. The second-order structure is the intra-urban inequality composite indicator that synthesizes the non-observed variables of the first-order structure. The model aims at demonstrating how to perform a theorized measurement of urban inequality so that it makes it possible to identify which dimensions most influence the others, as well as which dimensions are more relevant to this purpose.

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Section: Structural Equation Modeling (Sem)mentioning

confidence: 99%

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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“…estimates of drug use for ten local authorities; Gemmell et al 2004); while the SEBLUP produces more reliable estimates when the number of areas under study is large (e.g. estimates of perceived disorder for 282 neighborhoods; Buil-Gil et al 2019). Therefore, both ρ and m need to be taken into account to explain SEBLUP estimates increased precision in terms of RRMSEs, and SEBLUP estimates perform better as the number of areas under study increases.…”

Section: Results: Comparison Of Eblup and Seblup Estimatesmentioning

confidence: 99%

Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work

Buil‐Gil

Moretti

Shlomo

et al. 2020

Appl. Spatial Analysis

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There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these variables allow for using spatial random effects to improve small area estimation models and estimates' reliability. This article introduces the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from neighboring areas, to place-based policing. It assesses the SEBLUP under different scenarios of number of areas and levels of spatial autocorrelation and provides an application to confidence in policing in London. The SEBLUP should be applied for place-based policing strategies when the variable's spatial autocorrelation is medium/high, and the number of areas is large. Confidence in policing is higher in Central and West London and lower in Eastern neighborhoods.

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“…Regarding QoL indicators, the rich literature, e.g., [44,45] rarely used location-based networks [46] or geosocial media. One of the opportunities of geosocial media is to find information on perceived and real problems, supporting small-area estimations and enriching traditional large-area statistics and labor-intensive surveys such as [47]. Ref.…”

Section: Related Workmentioning

confidence: 99%

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Ostermann

2021

IJGI

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Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life.

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The geographies of perceived neighbourhood disorder. A small area estimation approach

Cited by 11 publications

References 36 publications

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work

Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

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