Today’s topic was ways to quantify landscapes in spatial analysis. Landscapes are impacted by a wide variety of forces. There is a concept in landscape ecology of how “form influences process and process influences form”. For example, this could mean how a forest fire (process) can change the landscape by removing stabilizing tree roots that lead to landslides and rotational slumps (form). This creates a constant feedback loop where the state of a landscape is always in flux. As a result, any given map is merely a snapshot of one of the possible patterns that might have emerged from a process.

One type of landscape metric is spatial autocorrelation, which is divided into first and second order processes. A first order process would be where patterns develop in response to some underlying environmental factor, while a second order process is when patterns emerged as interactions between objects.

Stationarity is another important metric, and is a measurement of to what degree processes may shift over changes in space. This implies a lack of directional bias, meaning that these processes are isotropic.

Patterns themselves have 5 classes of metrics into which they are categorized:

number of classes or cover types

texture measures

degree to which patches are compact or dissected

whether patches are linear or planar

whether patch perimeters are simple or complicated in shape

Today we went over how the spatial element is crucial in many analyses, from social to physical.

One major issue faced in geography is that of scale. Selecting the correct scale is crucial in any project and may completely change the nature of one’s results. This is commonly seen in the Modifiable Areal Unit Problem (MAUP), whereby selecting in inappropriately small or large scale results in skewed analysis results.

Selecting spatial unit boundaries is another issue that often occurs alongside scale-based problems and often serves to confound many projects. Using different boundary schemes may yield completely different, yet still correct, results. This in turn often leads to disagreements, as the data may be used for different purposes.

An example of this is gerrymandering, a practice created by 19th Century American politicians, where constituency lines are drawn in such a way as to ensure victory in a first past the post electoral system.

The extent of the study area must also be clearly defined, as selecting an overly large or small extent upsets the scale.

Spatial analysis must also take into account the effects of spatial autocorrelation. This is when a presence of some quantity expressed in one spatial unit is also seen to an extent in the immediately surrounding neighbour units. Spatial autocorrelation is non-random, meaning that there is often an underlying cause to it, and the degree of autocorrelation can be measured using Moran’s I, which provides a numerical score between -1 and +1.

In ecology, autocorrelation can often be explained by three neighbourhood models.

The first is a grouping model, where similar individuals choose to be located in a certain area, or are constrained in some way to be there.

The second is a group dependent model, in which the individuals in a given area are subject to similar external influences, such as a species of flower prospering in an area due to ideal physical conditions.

The final model is the feedback model. This is when individuals interact with and influence one another to become similar. For example, people who live together will inevitably develop shared interests or behaviours.

The main issue for spatial analysis is determining which cause is at work, especially since they may all be simultaneously true at differing scales.

UBC declared today as a snow day due to extreme weather conditions. No class!

Today we went over the basic concept of GIS, starting with the 5P’s: People, Perspectives, Processes, Patterns, and Places. These 5 are deeply interconnected and interact with one another in GIS analysis. The introduction of these 5 components highlights the importance of GIS in a modern world.

The class also introduced the main focal points of the course: landscape ecology, health geography, and crime geography, as well as the tools that we will be using in each. From the tools, it seems that this course will be highly statistical in nature.

Landscape Ecology

Landscape ecology looks at how living organisms interact with their environment, shaping their surrounds while simultaneously being shaped by them. This unit will focus on how landscapes change over time and how this affects the organisms that live in them.

Health Geography

Health geography is the study of health care and diseases in populations. On one hand, health geography looks at how to deal with distribution of health care, such as where to place hospitals and emergency services. ON the other hand, health geography also examines the spread of disease and health conditions, similar to epidemiology. In this field, health geographers map disease outbreaks, track vectors, and use geographical data to better understand how diseases can be prevented or treated.

Crime Geography

Crime geography maps out instances of crime and uses statistics with the aim of predicting and preventing future crimes. Data used in crime analysis is often compared with surrounding data to identify correlations between certain types of crime and certain elements. This was done in lab 3 of 370 where we looked at data for various crimes in Vancouver.