Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology

The current study spatially examines the local variability of robbery rates in the City of Saint Louis, Missouri using both census tract and block group data disaggregated and standardized to the 250- and 500-m raster grid spatial scale. The Spatial Lag Model (SLM) indicated measures of race and stability as globally influencing robbery rates. To explore these relationships further, Geographically Weighted Regression (GWR) was used to determine the local spatial variability. We found that the standardized census tract data appeared to be more powerful in the models, while standardized block group data were more precise. Similarly, the 250-m grid offered greater accuracy, while the 500-m grid was more robust. The GWR models explained the local varying spatial relationships between race and stability and robbery rates in St. Louis better than the global models. The local models indicated that social characteristics occurring at higher-order geographies may influence robbery rates in St. Louis.

Download Full-text

Principal Component Regression in Spatial Lag Model: Teen Employment in the City of Rosario, Argentina

Geographical Analysis ◽

10.1111/gean.12158 ◽

2018 ◽

Vol 51 (1) ◽

pp. 50-72

Author(s):

Alejandro Izaguirre ◽

Laura Di Capua ◽

José Luis Pellegrini

Keyword(s):

Principal Component Regression ◽

Principal Component ◽

Spatial Lag ◽

Spatial Lag Model ◽

Teen Employment ◽

Lag Model ◽

The City

Download Full-text

Bayesian Estimation of the Functional Spatial Lag Model

Journal of Time Series Econometrics ◽

10.1515/jtse-2019-0047 ◽

2020 ◽

Vol 12 (2) ◽

Author(s):

Alassane Aw ◽

Emmanuel Nicolas Cabral

Keyword(s):

Bayesian Estimation ◽

Linear Model ◽

Unemployment Rate ◽

Simulation Study ◽

Data Modeling ◽

Functional Linear Model ◽

Bayesian Mcmc ◽

Spatial Lag ◽

Spatial Lag Model ◽

Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

Download Full-text

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽

10.3390/atmos12020179 ◽

2021 ◽

Vol 12 (2) ◽

pp. 179

Author(s):

Said Munir ◽

Martin Mayfield ◽

Daniel Coca

Keyword(s):

Data Fusion ◽

Spatial Variability ◽

Urban Areas ◽

Road Traffic ◽

Dispersion Model ◽

Low Cost ◽

Spatial Modelling ◽

City Centre ◽

Small Scale ◽

The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

Download Full-text

Spatial analysis of the prevalence of obesity and overweight among women in Ghana

BMJ Open ◽

10.1136/bmjopen-2020-041659 ◽

2021 ◽

Vol 11 (1) ◽

pp. e041659

Author(s):

Killian Asampana Asosega ◽

Atinuke Olusola Adebanji ◽

Iddrisu Wahab Abdul

Keyword(s):

Public Health ◽

Hot Spots ◽

Information Criterion ◽

Health Interventions ◽

Spatial Lag Model ◽

Spatial Differences ◽

Body Sizes ◽

Lag Model ◽

Effective Public Health ◽

Average Body

ObjectiveIdentifying hot spots for the overweight aids in effective public health interventions due to the associated public health burden and morbidities. This study, therefore aimed to explore and determine the spatial disparities in the overweight/obesity prevalence among women in Ghana. The study also aims at modelling the average body mass index (BMI) values using the spatial regression and the performance compared with the standard regression model.DesignThis is a cross-sectional study using data from the 2014 Ghana Demographic and Health Survey (GDHS).SettingThe study was set in Ghana.Participants and methodsData on 4393 non-pregnant women aged 15–49 years from the 2014 GDHS. Both global (Moran’s I) and the local indicators for spatial dependence were examined through the mapped BMI values across the country by clusters. An estimated spatial lag model was used to explain the spatial differences in the average body sizes of women.ResultsThe overall prevalence of overweight/obesity among reproductive women in Ghana was 35.4%, and this was highly prevalent among educated women (p<0.001), those from wealthy households (p<0.001) and dwelling in an urban setting (p<0.001). Significant clustering (Moran’s I=0.3145, p<0.01) of overweight/obesity was observed with hot spots (clustering) in Greater Accra, Central, Western and Ashanti regions. The spatial lag model was the best fit based on the Likelihood ratio test and the Akaike information criterion and Bayesian information criterion values. The mean age of women and household wealth were significant factors accounting for the increase in the average cluster body size (BMI) of women and the spatial differences.ConclusionThe prevalence of overweight/obesity was high and spatially clustered in the southern, middle and coastal regions. Geographic specific and effective public health interventions and strategies are needed to address the growing morbidity burden associated with the rise in the average body sizes of reproductive women.

Download Full-text

How does foreign trade influence China’s carbon productivity? Based on panel spatial lag model analysis

Structural Change and Economic Dynamics ◽

10.1016/j.strueco.2018.08.008 ◽

2018 ◽

Vol 47 ◽

pp. 171-179 ◽

Cited By ~ 11

Author(s):

Lulu Zhang ◽

Lichun Xiong ◽

Baodong Cheng ◽

Chang Yu

Keyword(s):

Foreign Trade ◽

Model Analysis ◽

Spatial Lag ◽

Spatial Lag Model ◽

Lag Model ◽

Carbon Productivity

Download Full-text

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

Sustainability ◽

10.3390/su13169014 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9014

Author(s):

Yongjiao Wu ◽

Huazhu Zheng ◽

Yu Li ◽

Claudio O. Delang ◽

Jiao Qian

Keyword(s):

Carbon Emissions ◽

Industrial Development ◽

Industrial Structure ◽

Spillover Effects ◽

Spatial Lag Model ◽

Workforce Allocation ◽

Lag Model ◽

Using Data ◽

Shape Curve ◽

Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Download Full-text

Multi-scale patterns of spatial variability in sessile assemblage structure do not alter predictably with development time

Marine Ecology Progress Series ◽

10.3354/meps10273 ◽

2013 ◽

Vol 482 ◽

pp. 29-41 ◽

Cited By ~ 4

Author(s):

DA Smale

Keyword(s):

Spatial Variability ◽

Development Time ◽

Assemblage Structure ◽

Multi Scale

Download Full-text

Extending the Shared Socioeconomic Pathways at the City Scale to Inform Future Vulnerability Assessments — The Case of Boston, Massachusetts

Journal of Extreme Events ◽

10.1142/s2345737620500098 ◽

2019 ◽

Vol 06 (03n04) ◽

pp. 2050009

Author(s):

Jayne Lino ◽

Guillaume Rohat ◽

Paul Kirshen ◽

Hy Dao

Keyword(s):

Climate Change ◽

Global Scale ◽

Research Field ◽

Climate Change Research ◽

Multi Scale ◽

Vulnerability Assessments ◽

Neighborhood Scale ◽

The City ◽

Neighborhood Level

Climate change will impact cities’ infrastructure and urban dwellers, who often show differentiated capacity to cope with climate-related hazards. The Shared Socioeconomic Pathways (SSPs) are part of an emerging research field which uses global socioeconomic and climate scenarios, developed by the climate change research community, to explore how different socioeconomic pathways will influence future society’s ability to cope with climate change. While the SSPs have been extensively used at the global scale, their use at the local and urban scale has remained rare, as they first need to be contextualized and extended for the particular place of interest. In this study, we present and apply a method to develop multi-scale extended SSPs at the city and neighborhood scale. Using Boston, Massachusetts, as a case study, we combined scenario matching, experts’ elicitation, and participatory processes to contextualize and make the global SSPs relevant at the urban scale. We subsequently employed the extended SSPs to explore future neighborhood-level vulnerability to extreme heat under multiple plausible socioeconomic trajectories, highlighting the usefulness of extended SSPs in informing future vulnerability assessments. The large differences in outcomes hint at the enormous potential of risk reduction that social and urban planning policies could trigger in the next decades.

Download Full-text

Analiza zróżnicowania sytuacji kobiet na lokalnych rynkach pracy w Polsce w latach 2010–2016

Acta Universitatis Lodziensis Folia oeconomica ◽

10.18778/0208-6018.341.07 ◽

2019 ◽

Vol 2 (341) ◽

pp. 99-115

Author(s):

Karolina Lewandowska‑Gwarda

Keyword(s):

Seemingly Unrelated Regression ◽

Spatial Lag ◽

Spatial Lag Model ◽

Unrelated Regression ◽

Lag Model

Głównym celem artykułu jest ocena sytuacji kobiet na lokalnych rynkach pracy w Polsce oraz analiza jej zróżnicowania w czasie i przestrzeni. Podjęto w nim również próbę specyfikacji determinant badanego zjawiska. W analizach wykorzystano taksonomiczny miernik rozwoju, metody geograficznych systemów informacyjnych, metody eksploracyjnej analizy danych przestrzennych oraz wielorównaniowy model o równaniach pozornie niezależnych z autoregresją przestrzenną SUR‑SLM (Seemingly Unrelated Regression Spatial Lag Model). Badania przeprowadzono na podstawie danych statystycznych dla NUTS4 w latach 2010, 2012, 2014 i 2016. Na podstawie uzyskanych wyników zauważono, że zróżnicowanie sytuacji kobiet na lokalnych rynkach pracy w Polsce nie jest duże, niemniej jednak w nieco lepszej sytuacji są Polki mieszkające w okolicach stolicy oraz w zachodniej części kraju. Stwierdzono również, że sytuacja kobiet na lokalnych rynkach pracy nie zmieniła się znacząco w czasie. Dodatkowo potwierdzono, że nie tylko czynniki ekonomiczne, ale w dużej mierze również społeczne wpływają na analizowane zjawisko.

Download Full-text