Category Archives: Labs

In this lecture I learned about the application of GIS by the fire departments. GIS offers the ability to visualize geospatial data that are constantly being gathered by the fire departments. Visualization can be a very efficient way to communicate important information when disaster strikes and where each minute is extremely valuable. Using GIS, fire departments are able to determine the most suitable location by considering spatial variables such as proximity to different communities and response time as well as equipping important equipment such as ladders for different types of buildings in the area. Therefore, when a fire occur, GIS locates the nearest fire department and create the quickest route to the scene, saving valuable time for the fire fighters to extinguish the fire and save lives.

GIS also helps the fire departments by locating the exact location of fire hydrants instead of using addresses. This allows the quickest time needed for fire responders to access the water source to extinguish the fire. Additionally, it also makes hydrant maintenance convenient. Therefore, integrating GIS can definitely offer unlimited benefits for the fire department and allows them to visualize their complex data sets conveniently.

In this lecture I learned about the application of GIS in studying crime. GIS can aid authorities in multiple ways, the best use of GIS is visualizing the occurrences of crime in an area which is an important information in determining whether there is a spatial pattern or not.

In order to understand the geographic relationship with crime, three theories were raised in order to address this question: Routine Activity Theory, Rational Choice Theory, and Criminal Pattern Theory. Routine activity theory explains crimes committed base on routine activity of people, and for instance predicts that residential homes are burglarized during weekdays in the daytime, and commercial properties are burglarized during the weekend and nighttime hours. On the other hand, criminal pattern theory states that offenders are influenced by the daily activities and routines of their daily lives; they tend to concentrate in areas that are known to them. Lastly, criminal pattern theory helps us understand where and when the offence will occur. Thus, the three theories all involves geographic components that ultimately influence a criminal’s motive and the location where they are likely to commit a crime.

In today’s lesson, we discussed the application with GIS software in analyzing problems surrounding health geography. Four topics were introduced in the lectures which are: spatial epidemiology, environmental hazards, modeling health services, and identifying health inequalities.

Due to spatial heterogeneity in data, for example, population is unevenly distributed where people are free to relocate as well as the intrinsic characteristics of the population such as age and sex. Spatial epidemiology seeks to understand and describe spatial variation in disease risks specifically at an individual level and small areas.

For environmental hazards, GIS can also efficiently highlight existing environmental hazards within an environment through surveillance and identify population exposed as well as its impact. Through the multidimensional approach, GIS helps authorities identify vulnerable areas and create mitigation plans for the exposed population.

This lecture introduced us to health geography. Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. From previous lectures, I learned how geography is a fundamental element in spatial analysis, and it is closely linked to health geography as well.

There are three main themes in health geography categorized by: disease ecology, health care delivery, and environment and health. Disease ecology seeks to study infectious diseases through understanding their spatial patterns and distributions and how social, economic, and political factors play roles in affecting the diseases and how they affect changes towards dealing with them.

Health care delivery examines the social aspect of health geography through analyzing the distribution and spatial patterns of the provision and accessibility of health care to people in need. At the same time, through spatial analysis, it raises social concerns such as inequalities and specifically the health status and accessibility across space.

Lastly, environment and health is similar to the last concept in terms of its social implications. It addresses the impact of environmental policy on the health of communities and bring fourth the problem surrounding environmental justice. For instance, situating racial minorities in hazardous land that would severely affect their health and well being.

In this lesson we reviewed several fundamental statistical analysis techniques such as summarizing data using methods such as central tendency which involves the mean, median, and mode of the data. Also, we went over the measuring of dispersion such as skewness, which is a measure of symmetry. Skewness for a normal distribution is zero, and any symmetric data should have a skewness of zero. Data skewing towards the left is sometimes called a negatively skewed distribution because it’s long tail is on the negative direction on a number line, whereas right skew is where the the mean is typically less than the median.

I also learned about several statistical analysis such as the grouping analysis. This analysis is a powerful tool in GIS which can aid us and sort data into different groups/communities based on a set of quantitative variables. Different clusters will represent distinct characteristics from other clusters.

Regression analysis is another method which can be used to understand the relationships between different variables. There are two regression models I learned which were the ordinary least square model and the geographic weighted regression model. Ordinary Least Squares model is a global model aiming to minimize residuals. It is the proper starting point for spatial regression analysis, creating a single regression equation to represent the variable you are trying to understand. On the other hand, the geographically weighted regression model is a regression used to model spatial relationships of a given data set. This regression model is useful in working with large data sets with multiple features, for instance, working with multiple enumeration areas as a census data. One of the highlights of this regression model is that unlike the ordinary least square regression model, it adds a level of modeling sophistication by allowing the relationships between the independent and dependent variables to vary by locality. The GWR model is able to address this problem and construct a separate ordinary least square equation for every location in the data set determine by the kernel or bandwidth.

In this lecture I learned about the fundamental contribution of landscape metrics and how they influence the studies of landscape ecology. Landscape ecology is defined as the study of the reciprocal interactions
between spatial pattern and ecological processes that occur on
landscapes. One important concept that influences the relationship between ecology and landscape is spatial autocorrelation. Spatial autocorrelation is defined as the measure of how much close objects are in comparison with other close objects. Specifically, positive spatial autocorrelation is when similar values cluster together in a map and negative spatial autocorrelation is when dissimilar values cluster together in a map.

The lesson also addresses how spatial effects affect ecological data. For instance, climate affects the growth and distributions of tree species. Topography also influences local climate such as rain and radiation due to elevation, slope, and aspect. Other spatial effects mentioned were biotic interactions such as competition creating spatial patterns, human land use impacts, and other disturbance processes such as natural disasters like fires and floods.

In this lecture I learned about why geography is an important contribution to our research and analysis with GIS. The lesson highlighted several crucial factors surrounding the scale of a research perspective. Researchers must carefully select the geography of their problem and pay attention to how scale influence their decisions. Some of the problems the class investigated were the modifiable areal unit problem (MAUP), scale effect, and zoning effect.

The modifiable areal unit problem exist in a study where different statistical results can be obtained from the same set of data when the information is grouped at different levels of spatial resolution which this is considered the scale effect. In addition, another essence of the MAUP is the zoning effect, which variability in statistical results is observed as a function of the various ways these units can be grouped at a given scale.

The reason why geography is such a quintessential aspect of a research is because the geographical areas studied are always made up not of random groupings of species, individuals, households, but of  species, individuals.  households that tend to be more alike within the area than to those outside of the area. Therefore, I learned that I have to prioritize and consider the effect of scale when conducting any of my GIS research.

The article explores the topic of outdoor rape in Stockholm, Sweden and how different geographic variables may affect the relative risk in certain areas. The location of such crime is crucial in determining the risk of the victims, and three important elements were highlighted by the authors as main consideration for the criminals. First, accessibility,  in the sense of getting to, and knowing the crime scene and being able to flee it quickly. Second, opportunity, that is the presence of women, perceived as vulnerable, who either live in the neighbourhood or travel through it on their way home. Lastly, anonymity for the criminal act.

Therefore, three hypothesis were raised by the authors: 1) High counts of rape are associated with areas offering opportunity to the offender. 2) High counts of rape are associated with areas with poor surveillance and/or poor social control where residents have a high fear of crime. 3) High counts of rape are associated with areas that have good accessibility because they bring potential targets into an area and offer a quick and easy escape route from the crime scene for the offender.

Two statistical models were used in order to test how risk factors individually and jointly affect the risk of outdoor rape. 11 covariates were first identified from the three key categories which were accessibility, anonymity, and opportunity. For instance, whether subway station is present or not and the percentage of the population who fear crime and who avoid going out in the neighbourhood. The first model used was the Bayesian Hierarchical Poisson regression model for a “whole map” analysis and a Bayesian profile regression model for a localized analysis. From the first analysis, the researcher identified that the presence of a subway station in a basområde (small area) generally increases the risk of outdoor rape in an area by over 50 percent. Additionally, a basområde with a high level of population turnover tends to have an increased risk of rape.

However, risk factors such as lower average income showed a weak evidence to increased risk in outdoor rape. They also concluded that factors such as people’s fear of crime and the presence of forested or industrial areas do not appear to be associated with the risk of rape. The second model categorized the study area into seven clusters with relative risk of rape. From the result, the top 2 highest risk clusters represents area mainly in the city centre with high population turnover, small female residential populations and small numbers of schools with large number of liquor outlets. The 3 low-risk clusters represents primarily industrial areas whereas Clusters 1 and 3 are the affluent suburban areas with few alcohol outlets and few robbery cases, suggesting few micro-sites offering anonymity for the criminal act.

Overall, I believe this particular case study offered me a strong understanding to the risk factors to outdoor rape in Stockholm. Specifically, understanding certain covariates relating to the three theoretical constructs which were accessibility, opportunity and anonymity provided me information on why certain clusters represented higher rape risks. I was surprise that certain risks I had perceived as relevant resulted as insignificant in their finding such as forested area, which leads me to believe that other characteristics need to be present in order to constitute a ‘whole map’ risk factor. Therefore, I would rate this article 9/10 as they successfully identified different clusters within Stockholm and explained their relative risks using covariates which I believe were accurate factors which lead to outdoor rape.

Source:

Ceccato, V., Li, G., & Haining, R. (2019). The ecology of outdoor rape: The case of Stockholm, Sweden. European Journal of Criminology, 16(2), 210–236. https://doi.org/10.1177/1477370818770842

The article explores the environmental and non environmental factors that
contributes to a recent increase in typhoid fever cases in Fiji. The typhoid fever is caused by the bacterium Salmonella enterica serotype Typhi growing within the intestines and blood. With an estimated disease burden of 20.6 million cases in low- and middle-income countries in 2010, typhoid fever remains an enteric disease of public health concern in Fiji (de Alwis et al. 2018). There are many risk factors for transmitting Salmonella Typhi in Fiji and they are only partially understood. For instance, inadequate hand washing practices, poor sanitation, lack of access to safe water, and the dumping of untreated waste/sewage.

Moreover, during the cyclone season between the months of November to April, extensive rainfall and flooding can dramatically increase the risk of foodborne  and waterborne diseases. Therefore, in order to understand the causal factors, the authors used the presence of Vi-specific antibodies as a biomarker for typhoid fever exposure and used geospatial and statistical approaches to identify environment-associated risk factors in the general population of Fiji.

The authors first used a survey method where information for 44 variables was collected during the cross-sectional survey, they then selected 13 survey variables for this typhoid fever risk factor analysis on the basis of potential environmental risk factors of interest and potential confounding covariates. These variables included age, education, self-reported typhoid fever vaccination status, type of toilet at home, and type of sewage system.

The authors later used the multilevel mixed-effect logistic regression by including environmental and individual-related covariates as fixed effect and a random intercept in order to identify the risk factors. 16 environmental covariates were test and if the analysis showed at least moderate evidence of an association with seropositivity (p<0.5), these covariates were then used in multivariable analysis as a continuous variable. Therefore from the test result, 4 environmental variables showed significant association to the increase cause in typhoid fever, which were annual rainfall, rainfall during the wettest month, work location, and rainfall during cyclone season.

Moreover, the authors then conducted a hotspot analysis using information gathered from the Global and Anselin local Moran I test to identify statistically significant spatial clusters. High-seroprevalence communities (hotspots) were detected only in Viti Levu, whereas typhoid fever appeared to be more homogeneously distributed in Vanua Levu, suggesting a different transmission pattern on the two islands. Therefore, the study demonstrated a spatially heterogeneous exposure to typhoid fever.

Overall, I would give this study a score of 8 out of 10. The reason is because I
believe the authors chose the correct approaches in trying to identify the underlying causes of the typhoid fever outbreaks in recent years in Fiji. The hotspot analysis gives readers strong understanding in seeing whether there are spatial clusters within parts of Fiji. Additionally, the multivariable regression analysis successfully identify the variables contributing to Typhoid fever, which is very helpful for local authorities to uses this information to address a practical solution. However, the authors themselves had identify some knowledge gap within this study, such as the uncertainty around whether the Vi antigen is the absolute proxy for Salmonella Typhi infection. Additionally, the cluster analysis was hindered by an uneven distribution of the surveyed communities. Therefore,
improvements could be made to this study.

Source:

de Alwis, R., Watson, C., Nikolay, B., Lowry, J. H., Thieu, N. T. V., Van, T. T., …Cano, J. (2018). Role of Environmental Factors in Shaping Spatial Distribution of Salmonella enterica Serovar Typhi, Fiji. Emerging Infectious Diseases , 24 (2), 284+. Retrieved from http://link.galegroup.com.ezproxy.library.ubc.ca/apps/doc/A533000198/HRCA?u =ubcolumbia&sid=HRCA&xid=8629fe44

The topic of this study is to explore the Swedish landscape composition and its effect on butterflies at different spatial scales. Using data from an ongoing national monitoring program of butterflies in Sweden, the study raised several questions to be addressed which are: (1) How do matrix composition influence the occurrence of butterfly species in semi-natural grasslands? Can different land cover types in the landscape (semi-natural grasslands, arable land, forests, and water surfaces) explain the occurrence of butterfly species in semi-natural grasslands? (2) At which spatial scale do butterflies respond to different matrix types surrounding the semi-natural grasslands? (3) Can life history traits explain the spatial scale of response?

Though habitat area and isolation have been shown to have strong effects on the local biodiversity, a substantial body of research also shows that species abundance and composition in remaining habitat patches are strongly affected by the surrounding matrix. Other species differences have been “attributed to life history traits, e.g. species with low reproduction, low dispersal ability and specialist host plant requirements have been shown to be most sensitive to habitat loss (Bergman at el, 2018).”

They used generalized linear model for each land cover matrix types which are, arable land, semi-natural grassland, forest, and water surfaces to estimate the odds of finding a specific species in a site based on the proportion of each land cover type. The generalized linear model is a simple yet effective method for this study particularly because it allows us to understand whether land matrix have a strong positive or negative effect on species richness across different spatial scales reflected by the median z score. This was shown in the result which for example, they found a significant positive correlation between the amount of semi-natural grasslands and the amount of arable land at the scale of 631 m and higher (highest Pearson’s r = 0.49, p = 0.0002 at the 40 km scale).

In addition, there was also a small negative correlation between the amount of semi-natural grasslands and the amount of forest cover at the smaller to medium scales (from 100 m to * 3 km). Thus, the significance of the relationship is a strong evidence to support the main argument which is whether landscape matrix affects richness in butterflies.

Overall, I am definitely convinced that there is validity to the author’s main argument. The charts contained in the study clearly depict the clear relationship between the landscape matrix and species richness using median z score at different scales. I would rate this paper 8 out of 10. The reason is because the authors successfully answered the three study questions they raised and the entire study was easy to understand and follow. I did not give it a perfect score simply because I believe there are other factors that explains species richness. For instance, some butterflies may simply be specialists that thrives in a particular landscape type, while other butterflies can be generalists, which the study did not account for this.

Source:

Bergman, K.-O., Dániel-Ferreira, J., Milberg, P., Öckinger, E., & Westerberg, L. (2018). Butterflies in Swedish grasslands benefit from forest and respond to landscape composition at different spatial scales. Landscape Ecology, 33(12), 2189–2204. https://doi.org/10.1007/s10980-018-0732-y