This is an exciting topic which studied at the air quality in Mumbai city, India. My peer gave a detailed presentation on the background of Mumbai, which is the most populated city in India and the fifth most populated in the world. The air quality in the city deteriorated from 2004 to 2012, and the potential influenced factors included polluting factories, better cars, and better fuel options. The objectives and method are also clearly demonstrated in the presentation. The first objective is to interpolate the air quality in regions where does not have data from the monitoring station, and the second objective is to determine the economic costs of. Morbidities caused by air pollution. Then, I really admire that the research conducted three types of GIS interpolation to predict air quality. However, it is a pity that the study did not give a clear visualization to present the air quality results. The table and graphs cannot represent the complete results and audience may understand the effect better by showing the interpolating raster maps. On the other hand, my peer indicated the research could be flawed because of limited station collecting data and incomplete influential variables.

Ye, C., Chen, Y., & Li, J. (2018). Investigating the influences of tree coverage and road density on property crime. Isprs International Journal of Geo-Information, 7(3), 101. doi:10.3390/ijgi7030101

The research is conducted to discover the correlations between tree coverage and property crime, and road density and property crime in the city of Vancouver. The researchers argued that crime analysis, prevention, and mapping have benefited from the quickly developed GIS techniques. Previous studies have analyzed some factors that affect crime rates such as population density, poverty level, unemployment rate. However, the impacts of vegetation density and road density on the crime rate have long been under debate, especially in Canada. Hence, the research hoped to fill in the blank and further supported decision making in urban property crime prevention.

For the vegetation coverage data, the research obtained a 2*2m high spatial resolution 2013 orthophoto. Researchers classified the vegetation coverage area and conducted an accuracy assessment. The producer’s and users’ accuracies reached 96.9% and 99.9%, respectively. For road density, they get data from the road network data from the Statistic Canada, and the density was calculated as the ratio of the sum of the road lengths to the land area. Data also included other factors that have already been proved related to crime in the previous studies, including population density, income, a lone parent, unemployment rate, light density, and graffiti. All the data were used as the dependent variable of the crimes in the later regression models.

The research used the cross-sectional analysis by using GeoDash and ArcGIS software. First, it calculated the global Moran’s I in GeoDash to examine the spatial autocorrelation of crime incidents. Then, it conducted an Ordinary Least-Square (OLS) regression model in GeoDash to examine the correlation between crimes and dependent variables. The OLS model ran for total property crime (theft, mischief, and break-in), theft crime and break-in crime respectively. Then, since the OLS ignores the spatial autocorrelation of crime data, a spatial lag model was applied to examine the spatial dependence. Finally, Geographically Weighted Regression (GWR) tool in ArcGIS was used to examine the local correlation at the DA’s level.

The OLS results showed significant negative correlations for both vegetation coverage and road density with each type of crime rate. However, the R2 index for both three types of crime is lower than 0.2, meaning a low fitness of model. The spatial lag result increased the R-squared value, confirming a better performance. The GWR result showed an overall better performance compared to the OLS model. Hence it had various local coefficients. The GWR reflected a negative relationship between poverty crime rate and the vegetation coverage rate closer to the downtown area while showed a weak positive correlation in Stanley Park. Moreover, for road density, there is always a negative correlation.

Overall, the research accomplished the goal to discover the spatial pattern and variation between tree coverage and property crime, and road density and property crime in the city of Vancouver. They also admitted the limitations of research. For example, the study did not differentiate urban trees along streets and the trees in parks, Planning trees in a different location may have a different influence on the crime rate. The research gave suggestions on designing more complex road system and planning more trees in the downtown area in the aim of crime reduction. Overall, I give the research 8/10.

Jia, Z., Jia, X., Liu, Y., Dye, C., Chen, F., Chen, C….Liu, H. (2008). Spatial Analysis of Tuberculosis Cases in Migrants and Permanent Residents, Beijing, 2000–2006. Emerging Infectious Diseases, 14(9), 1413-1419.

Tuberculosis (TB) is an infectious disease that most often affects the lungs, spreading through the air. The incidence of TB had been controlled to 7 cases per 100,000 population in Beijing during the last 90 years but remerged in the early 2000s. Previous studies had indicated that the reemergence of TB was correlated with the increasing migrants. Big data of the TB cases obtained from the Beijing Institute of Tuberculosis Control also showed that an increasing trend of the prevalence rate in TB in Beijing in the early 2000s is following the increasing migrant population. However, due to the limitation of the techniques, few previous studies displayed the spatial distribution of TB. Hence, based on previous studies and data, the authors made an argument that the migrant population contributed to the prevalence of TB in Beijing. The purpose of the study was to present the hotspots distribution of TB for both permanent and migrant residents at the district level and examined the impact of the migrant population on the reemergence and transmission of TB in Beijing.

The study site covers 18 districts of Beijing, around in total 16800 square kilometers surface area. Data for the study included all TB cases reported during 2000-2006 sourced from the Beijing Institute of Tuberculosis Control, and the demographic data of permanent residents and migrant population for each district in Beijing from 2000-2006 census, provided by Being Municipal Public Security Bureau.

A GIS-based spatial analysis was used to indicate the spatial distribution of TB and highlighted the hot spot areas.  Global Morans’I statistics is to discern the spatial autocorrelation of TB cases, and Getis’s Gi statistics is to find out the TB hot spots. Both Morans’I and Getis’s Gi specified 10 km as the threshold of distance. Then, a couple of 2-level poison regression models were used to detect the difference of prevalence of TB among 18 districts and the origins of the population.

Results indicated that the hot spots among the prevalence rate of migrant population persisted in four central urban districts from 2000 through 2006. However, for permanent residents, there was only one hot spot detected in 2003. The results implied that the migrant population in Beijing dominated the prevalence of TB. Besides, the Poisson regression models showed there was a significant difference in TB prevalence at the district level, which was associated with the origin of the cases. Migrants from the western zones of China had the highest prevalence rate, compared to migrants from any other zones.

Overall, the study successfully exhibited the spatial distribution and hotspots of TB prevalence for both permanent residents and migrant population, which helps make TB control strategies. However, due to global Moran’s I statistics, the study failed to display the trend of TB prevalence in a smaller census unit. Though regression models show the origin of migrants contributed to the difference of TB prevalence rate at the district level, the models did not give details on how the migrants from different zones impacted on the prevalence rate for each district. Also, the study divided the population into permanents and migrants but ignored the impact of interactions between each other. The limitations above could be improved by using a local geographically weighted regression rather than the global Moran’s I. In general, I would like to give the study a 6.5 score.

Schüßler, D., Lee, P. C., & Stadtmann, R. (2018). Analyzing land use change to identify migration corridors of African elephants (Loxodonta africana) in the Kenyan-Tanzanian borderlands. Landscape Ecology,33(12), 2121-2136. doi:10.1007/s10980-018-0728-7

The purpose of the research was to analyze the land cover changes in the Kenyan-Tanzanian borderlands of the greater Amboseli ecosystem and used African elephants as an indicator species to evaluate landscape connectivity. Standing at the perspective of modern conservation planner, the researchers concerned about the impacts of recent-year agricultural expansion on the seasonal migrations of large herbivores.  In the article, researchers argued the landscape connectivity of elephant population had been disrupted by the land use changes.

The study site is located in the intersection of southern Kenya and northern Tanzania, with a greater Amboseli ecosystem about 10, 000 square kilometers. The research was decided into two main parts. The first part based on a GIS method by using multiple -temporal Landsat imagery from USGS and a supervised maximum likelihood classification, to monitor the land cover changes over a 43-year period from 1975 to 2017.  Seven different land cover classes including water, open vegetation, semi-open vegetation, closed shrubs, forest, agriculture, and clouds were selected,  according to previous studies in Amboseli. The second part contained a literature review of overall 72 articles to measure 8 main paths of elephant migrations. Then, based on the classification result and the measured migration paths, the landscape connectivity was evaluated.

The result showed that the land cover changed considerably during these years. The woodland and bushland declined and replaced by open grasslands and agriculture lands. The increase of small-scale cultivation and the loss of vegetation density resulted in a landscape fragmentation. Only one of the eight corridors was formally protected. Therefore, the researchers concluded that wildlife habitats and migration corridors were significantly disrupted, which successfully proofed their argument.

In general, the research provided relatively convinced land cover change maps. First, it used in total 14 Satellite imageries to conduct classification. The multiple imagery references displayed smooth changes in land cover, increasing the credibility. In addition, the accuracy quality of supervised classification was examined and fixed through three different ways: visually compared map with the satellite images, using literature review as a reference and calculated the overall accuracy for each time stage. However, the credibility of the elephant migration corridors was insufficient. The corridors were totally identified by literature reviews and the most references were from the same author. The predicted corridors in the research were only straight and rough lines. Actually, the credit could be increased by using GIS to predict the possible migration corridors. Through producing a cost surface, the corridors can be displayed in a more accurate and detailed way. The literature review can still be used as references for accuracy assessment.

Overall, the research successfully provided the land cover changes in southern Kenya, but the accuracy of landscape connectivity may be impacted by the measurement uncertainty of migration corridors. Hence, I would like to give the paper a 7/10 score.