For me this was one of the most interesting and exciting lectures as we got to see in great detail crucial real-world GIS applications. This lecture used the example of the Calgary Fire Department and their use of GIS in planning for the future, deciding where to locate new stations and apparatus deployment. GIS was used for mapping neighbourhood fire risk building by building based on concentration of structures and distribution.

Other applications included historical fire mapping to determine neighbourhood risk, hydrant mapping, station mapping and estimated response times. Station mapping is particularly important to understand how fast an apparatus can reach the nearby allocated buildings, which routes it can take and which hydrants it can use. It is also used for determining where to locate new stations based on response times and expected population growth and neighbourhood developments.

Often fire departments have to respond to emergencies other than fires, so strategical placement of fire trucks and real-time incident monitoring is also quite important. Overall a lot goes into planning where to locate fire stations, hydrants and trucks as they determine the safety of neighbourhoods.

As mentioned multiple times before, GIS is a great tool for visualizing various spatial patterns. Crime is undeniably a geographic phenomenon (although some might disagree) and should also therefore be analyzed in terms of its spatial distribution.

In this lecture we looked at three different theories of environmental criminology: routine activity, rational choice and criminal pattern. In the routine a crime occurs as a combination of an offender willing to commit a crime, a suitable place for that crime and the presence of a target in that place at the right time. It assumes that people follow routines and the transition between different places as a routine creates a crime opportunity. The rational choice theory assumes that offenders make a rational choice of committing a crime when the benefits outweigh the consequences and probability of being convicted. Lastly, the criminal pattern theory assumes that offenders are more likely to commit a crime in areas and situations that are familiar to them.

The main goals of environmental criminology is being able to predict and explain the spatial patterns and occurrences of crimes and offenders. One way of addressing these goals is geographic profiling which is a tool that will help determine the likely location of criminals based on the locations of crimes. Serial criminals can be classified into 2 spatial groups: marauders (which commits crimes in their own neighbourhoods or in proximity) and commuters (which travel to other neighbourhoods to commit crimes).

There are four major applications of GIS in health geography: spatial epidemiology, environmental hazards, modelling health services, and identifying health inequalities. This lecture went more in depth on spatial epidemiology which examines the spatial variations in various disease risks. In general, small areas are used for epidemiological studies as they can include less confounding factors, which in turn can give more accurate explanations for found correlations. Common issues in epidemiological studies include the misalignment of spatial units and uncertainties related to the quality, compatibility, and availability of data points.

GIS for environmental hazards is used to determine their causes as well as possible mitigation factors. Modelling health services and identifying health inequalities are closely related in health geography. Often health inequalities become the determining factors for modelling health service distributions.

Epidemiology can be divided into that focusing on health and the importance of location in determining it, and disease and how one is identified. Identifying a disease can be difficult, so in order to study it we have to be able to measure its occurrence.  Some common measures include counts, prevalence, incidence and mortality.

The health of individuals is intrinsically linked to where they live by a series of factors: such as the food they eat, the viral strains to which they are exposed, and the atmospheric conditions in which they live. This lecture talks about the differences between medical and health geography.

Medical geography preceded health geography and is important to understand to grasp the broader concept of health geography. Medical geography utilized GIS knowledge to map the biological and ecological determinants of diseases. It is holistic in the way it views its subjects. This perhaps is one of the main differences between medical and health geographies. Health geography includes the biomedical aspects, but does not end there, as it encompasses social and environmental influences on the development and distribution of health-related issues.

Medical geography is the more “traditional” way of examining diseases, where they are believed to occur regardless of the socio-economic or political factors. Health geography or “contemporary” medical geography considers the humanistic factors associated with health-related spatially distributed phenomenon.

There are five strands to health geography: spatial patterning of disease and health, spatial patterning of service provision, humanistic approaches to “medical geography”, structuralist/materialist/critical approaches to “medical geography” and cultural approaches to “medical geography”. It is also divided into two stream, one of which focuses on epidemiology and the other on health services provision.

Understanding statistics is critical in GIS as we need to summarize and explore datasets and be able to identify relationships and make predictions. In this class we reviewed the kinds of data that exist, ways of summarizing it and the two main ways of looking for relations: visualizations and quantitative approaches.

One of the most important quantitative approaches in GIS that we discussed in class is regression modelling. It helps answer why we observe certain spatial patterns and if the patterns are significant or just a result of random outcomes. A number of methods can be used, such as ordinary least squares which can help determine the most important variables from the AIC, and the geographically weighted regression which we describe and apply in detail in lab 3.

Geography and location are fundamental in any analysis that is being conducted. In landscape ecology the focus is on the interactions between ecological processes and observed geographical patterns, how they influence and determine each other. Some methodological consideration for landscape ecology are first- (pattern as a result of environmental factor) and second-order (pattern a result of interactions) spatial autocorrelation processes, and first- (intensity constant over space) and second-order (absence of interactions) stationarity.

The main types of processes affecting landscape ecology have been discussed in lecture: abiotic, biotic, anthropogenic and disturbances. Climate, topography and soils are some of the major abiotic factors considered in landscape ecology analysis. Biotic factors include interactions between species, such as competition and predation. Anthropogenic or human influences could be such things as deforestation and urbanization. Lastly, disturbances are various natural hazards such as volcanic eruptions, fires and floods drastically change landscape ecology. Landscape ecology is determined by a combination of all these factors and the different scales that we look at them. Three main factors can be used to explain differences in spatial patterns: local uniqueness, phase differences and dispersal.

Studying geography is critical, as it often underlies other types of studies which feature any other sort of social, environmental, cultural, or economic concern. The geographic implications of a problem are an inherent factor in any analysis, and its absence can lead to unrealistic conclusions. This leads to MAUP, or the “modifiable areal unit problem”, which states first that the scale or spatial resolution of spatial analysis can lead to different statistical results, and secondly that the way that data points are aggregated into clusters can lead to different statistical results. Therefore, MAUP describes a powerful statistical and geographical issue inherent in analyzing a list of geographical data points. The MAUP is an intrinsic characteristic of all physical and abstract geographic studies.

Another example of the importance of geographical analysis in studies is the Simpson’s paradox, also called the reversal paradox or the amalgamation paradox, which describes a situation in which related trends can be found in separate groups of data, but an opposing trend can be found when those groups are combined. In conclusion, an understanding of the geographical influence in a data set is often critical before any other social, environmental, or biological conclusions can be made about a scientific analysis.

GIS is useful for finding social and environmental patterns in different geographical locations. Once spatially based data has been collected, it can be analyzed to find where things occur the most, why it occurs in these locations, and how these clusters affect other contextual data. All of these conclusions can be used to optimize where things should be located within cities, communities, or any other location under study. In this class, we looked at three areas of study which were drastically improved using GIS tools: landscape ecology, crime analysis, and health geography. These three areas of study are linked by the five “p”‘s: patterns, processes, places, people and perspectives, which we will examine further in this course.

Landscape ecology is the study of how landscape patterns affect the ecological processes within a relatively local environment. The response variables used in landscape ecology statistics are abundance, distribution, and process variables. Health geography combines genetics, individual lifestyle choices, and environmental factors to find statistically based conclusions regarding disease ecology, health care delivery and accessibility, and the interaction of environmental risk and community health. Finally, crime analysis using GIS results in a more efficient crime prevention force by analyzing crime patterns and trend correlations. Analysis of crime trends has given support to theories such as the social disorganization theory, the rational choice theory, and the broken windows theory. GIS is the best method for analyzing this type of information because it combines the spatial data collection with scientific analysis and the computer software to display all the information in an easy to digest way.