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Geography Patterns Places
Landscape Ecology Processes
Crime Analysis Processes
Health Geography
Processes
People
Perspectives
Develop vs protect
Emergent processes
HMO vs patient
Scale
Social theory
CONTEXT
• Where do things happen: patterns: clusters, hot spots, disparities.
• Why do they happen where they happened: location decisions: places, processes.
• How does where things happen affect other things (context, environment) and how does context affect what happens: interactions
• Where should things be located: optimization
• Fragstats: Landscape ecology metrics that provide summaries of fundamental attributes such as area, perimeter, shape, core area, etc., as well as metrics that summarize the distribution of classes across the landscape, such as connectedness, fragmentation, evenness and contrast.
• Those metrics have import in a large number of contexts.• For example, we learned in a paper review last week that
edge contrast is important in relation to residential burglaries—homes are more likely to be broken into if they are adjacent to an industrial area than if there are adjacent to a natural area.
• Geographically-weighted regression: puts the ‘local’ into regression analysis, which allows for geographic patterns to emerge. In place of a ‘one view accounts for all’ global perspective, many different local perspectives can be achieved (wherein, for example, the impact of independent factors can change from being positive to negative).
• Cluster analysis: showing the commonalities and the differences between regions across space.
• A key consideration: Visualization can lead to hypothesis generation.
• Crimestat: providing multiple insights into point and areal dependencies--beyond metrics that simply quantify the spatial distribution of points (e.g., nearest neighbours, spatial autocorrelation) one can examine the intensity of points, as points themselves (e.g., fuzzy measures, nearest neighbor hierarchical clustering), as well as the intensity within areal units (e.g., correlograms).
• You can also ‘summarize’ the distributions by using kernel density methods to create hot spot maps.
• Important consideration: whether to examine the points alone or within the context of potential influential factors (e.g., dual kernel using an intensity variable)
Issues such as:
• the scale, grain and extent of a study area,• the modifiable areal unit problem (S.A.),• the nature of the boundaries of a study area, and• spatial dependence / heterogeneity (S.A.)
are implicit in any spatial analysis.
• The ultimate aim is to understand process through an examination of form (e.g., landscape pattern, crime incidents, disease outbreaks). Such knowledge can be used in both a descriptive and prescriptive manner.
• In order to understand what quantification of patterns can tell us about the processes that have occurred / are occurring, we should first consider the potential causes of those patterns.
• Patterns develop if the objects / events we are studying exhibit spatial autocorrelation (that is, if the objects / events were randomly distributed about the landscape no meaningful patterns would be observed).• If the patterns develop as a result of a response to an environmental
factor (such as soil, slope position, water bodies), this is referred to as a first-order process.
• If the patterns develop as a result of interactions between the objects / events themselves (such as contagious diseases), or dispersal / diffusion limitations, this is referred to as a second-order process.
Environment Biology Unexplained
Spatial autocorrelationLink
• Many spatial scientists take the view that an observed map pattern is (only) one of the possible patterns that might have been generated by a hypothesized process.
• Statistical analysis then focuses on issues around the question:• Could the pattern we observe have been generated by this
particular process?
• Which leads to: • A spatial process is a description of how a spatial pattern
might be generated.
• Landscape ecology, a subdiscipline of ecology, is the study of how landscape structure affects the abundance and distribution of organisms. Landscape ecology has also been defined as the study of the effect of pattern on process(Turner 1989), where "pattern" refers specifically to landscape structure. The full definition of landscape ecology is, then, “the study of how landscape structure affects (the processes that determine) the abundance and distribution of organisms.”
• In statistical parlance, the "response" variables in landscape ecology are abundance /distribution /process variables, and the "predictors" are variables that describe landscape structure.
Envi
ronm
ent
Org
anis
ms
Spac
e
Scale-dependent response: The scale of response to the environment depends
on the organism.
Spatial autocorrelation: Biotic processes such as dispersal or
interactions create patchiness in organism distributions.
Spatial dependence: Physical processes create spatial structure in
the environment, including deterministic structure in organism
distributions. Space
OrganismsEnvironment
Figure 3 in Wagner and Fortin, 2005
Hedgerow network landscape illustrated with color infrared imagery and woodland / treerowsoverlay in green.
• These metrics measure what is present and their relative amounts or proportions, without reference to where on the landscape they may be located.• Relative richness--the proportion of the number of cover types
potentially present (e.g., based on the past or other landscapes).
• Dominance--the deviation from the maximum possible evenness.• Diversity—a reflection of richness and how evenly the
proportions of cover types are distributed.• Connectivity--based on a user-defined threshold, a measure of
how connected the patches are (think of movements between two patches).
• Probabilities of adjacency--the probability that a grid cell of cover type i is adjacent to cover type j.
• Contagion--distinguishes between overall landscape patterns that are clumped or rather dissected.
• Connectivity--how fragmented is a habitat type.• Proximity index--the degree to which patches in the landscape are
isolated from other patches of the same cover type.• Area-weighted average patch size--to account for the frequently
observed skewed distribution in patch sizes (e.g., many small, few large), use an area-weighted average to better reflect the probability of randomly selecting a patch.
• The role of place, space and community in shaping health outcomes and health care delivery
• There are three main themes in the geography of health care (or medical geography): • disease ecology, • health care delivery, and • environment and health.
• Disease ecology involves the study of infectious diseases (e.g., malaria, HIV/AIDS, infant diarrhea), including the spatial distributions of meteorological, biological and cultural phenomena associated with disease, as well as the social, political and economic barriers to positive change.
• The study of health care delivery includes spatial patterns of health care provision and patient behaviour and includes issues like inequalities in health (health status and accessibility), and de-institutionalisation of the mentally ill.
• Environment and health is a focus for health geographers which draws geography's long tradition in environmental hazards research together with health geography. Topics within this tradition include environmental risk assessment, as well as the physical and psycho-social health impacts of environmental contamination and the impacts of climate change on society.
1. Spatial epidemiology
2. Environmental hazards
3. Modeling Health Services
4. Identifying health inequalities
Aerosol optical depth, Delhi
Aerosol optical depth, Delhi
• Geographic research in health is often dichotomized between quantitative and qualitative methodologies, with quantitative studies closely aligned with epidemiology and qualitative studies aligned with medical sociology and social sciences (Dummer, 2008).
Although differences in approach are important for the assessment of complex problems, these methodologies share a common theme: the role of place and space in health.
http://geography.asu.edu/education/degrees/sc_05.htm
• A set of systematic, analytical processes directed at providing timely and pertinent information relative to crime patterns and trend correlations to assist the operational and administrative personnel in planning the deployment of resources for the prevention and suppression of criminal activities, aiding the investigative process, and increasing apprehensions and the clearance of cases.
• Within this context, crime analysis supports a number of departmental functions including patrol deployment, special operations, and tactical units, investigations, planning and research, crime prevention, and administrative services (budgeting and program planning). (Steven Gottlieb et al., 1994, "Crime Analysis: From First Report To Final Arrest.“)
• Historically, the causes and origins of crime have been the subject of investigation by a variety of disciplines. Some factors known to affect the volume and type of crime occurring from place to place are: • Population density and degree of urbanization, site locality and its
surrounding area. • Variations in the composition of the population, particularly youth
concentrations. • Stability of population with respect to residents' mobility, commuting
patterns, and transient factors. • Modes of transportation and the highway system. • Economic conditions, including median income, poverty level, and job
availability. • Cultural factors and educational, recreational, and religious
characteristics. • Family conditions with respect to divorce and family cohesiveness. • Climate (climate change and crime)
• http://www.tempe.gov/cau/about_ca.htm
• Environmental criminology• Routine activity theory
• Social disorganization theory• Rational choice theory• Broken windows theory
http://www.umbc.edu/geography/student_projects/digital_atlas/index.htm
Administrative CA
Strategic CA
Tactical CA
Intelligence and Criminal Investigative Analysis
High
Low
These studies typically involve examining geographical variations in exposure to environmental variables (air, water, soil, etc.; socio-economic factors) and their association with ecological / health / crime outcomes while controlling for
other relevant factors using regression.
• Over the past three months you have explored landscape ecology, health geography and the crime analysis and, in the labs, performed spatial analytical methods used in each. In doing so, you have been exposed to some of the software programs typically used in these fields (in conjunction with a GIS such as ArcMap).
GIS
Data
Tools
Methods
Needs
Human goals & aspirations:Shelter, food, safety, health,curiosity, recreation
Data related to space:Maps, remote sensing,transects, collections
Computer technology:Hardware, software,networks
Scientific theories:Land management,geography, biology, math, computer science
• In order to manage spatial data you need to know spatial data and know how spatial analysis differs from aspatialanalysis.
• Management based on spatial data needs to develop from a foundation of spatial data awareness—what forms can spatial data take (e.g., raster, vector) and in what form should the spatial data be in (different forms lend themselves to different analytical approaches), how best to approach the decision-making process (of the myriad spatial analytical tools that are available, which ones would be the best to use), and how to present the results of such analyses
If geography matters, GIS can be used to study the problem.
• Which species will be affected by the new hydro corridor?
• What wetlands are most important for the tundra swan?
• Which areas will be most affected by climate change?
• What areas are associated with higher rates of skin cancer?
Human GeographyPopulation Diseases Arts
Immigration
Natural EnvironmentPrecip Soils Geology
Hydrology
Economic ActivityPipelines Telco Roads
IndustryPower Network
Natural ResourcesWetlands
Mines Parks Oceans Forests
GIS integrates disciplines and technologies such as remote sensing, surveying,
photogrammetry, spatial analysis, cartography, computer science.
GIS integrates disciplines.
SurveyingMonitoring
Descriptive Mapping
ModelingPlanning
Prescriptive Mapping
• Your assignments encouraged you to question research, and to research your questions.
• Questions encourage thinking and learning: • Why is it there? (topography, land use, adjacent features)• What are the constraints on its distribution? (topo, zoning)• Is it linked to other phenomenon? (pop density, edges)
• Coming up with good questions requires skills in observing, defining, classifying, analyzing, inferring, reasoning, integrating, and associating phenomena.
• Location / extent:• Where is the phenomenon of interest?• What is the phenomenon there?• Why is it there?• How much of it is there?
• Distribution / pattern:• Is there regularity in its distribution?• What kind of distribution does it make?• Where is it in relation to similar phenomenon?
• Over the past months you’ve been encouraged to think geographically about issues in landscape ecology, health geography and crime analysis.
• It should be apparent that there exist considerable linkages between the fields—while the examples were specific to each field, the ‘geography’ and analytical methods were often similar.
• How does GWR “extend” traditional regression analysis? Provide an example of its application for spatial analysis.
• Landscape ecology is concerned, in part, with patches and quantifying patchiness. Define those terms, and outline how you can look at those concepts using some of the tools/methods used in this course.
• What are some of the measures of spatial autocorrelation mentioned in class / used in the labs. Indicate the ranges associated with the measure (e.g., 0-1) and the meaning associated with the values.
• What does a hotspot map tell us?
• Describe stationarity. Is it an issue in spatial analysis?• The theme that patterns reflect a process was used as the
integrating concept in this course. Outline how this theme works across the three fields of research presented in this course.
• There are methods of spatial analysis designed for raster data and others for vector data. Provide a description of some of those methods.
• Describe the modifiable areal unit problem. How does it relate to the fields of research discussed in this course?
• Normalizing variables, and taking into account (e.g.) populations at risk, are two important (and related) concepts. Why are these methods often very useful to employ? Provide examples illustrating how they impact the interpretation of the results of a spatial analysis.
• Place and space are two related but different concepts that managers need to be fully aware of. Describe the two concepts and present a scenario where knowledge of the two could make a significant difference in the outcome.
• Final Exam APR 4 2020 starting at TBD. Specific details will be posted on CANVAS.
