Geographies of health
Lecture 1 introduction
What is health > definitions
Not just healthy but also responsibilities and demands (definition 2005)
Objective measure or subjective measure
Can be individual or in population (like a population condition)
Historic background:
John snow: studied cholera outbreak > spatial mapping for health
Health:
Illness is subjective experience; your feeling tired, sad etc. but that’s
not diagnosed
Illness can become a disease if its related to specific symptoms
Sickness
Epidemiology:
Basic science of public health
Why is health geography different from epidemiology (test question)
Focuses on distribution, determinats, specified populations
Why do people in this area have different problems than in other
areas
Incidence and plrevalence
How many new people get the number of a new disease (covid >
how many new people get infected)
Prevalence
Morbidity and mortality
Mortality Number of people dying per population
Morbidity
Chronic and acute
Chronic problem: long lasting health problems (health diseases,
diabetes)
, Acute: more abrupt, like a heart attack, u need treatment
immediately
Diseases and causes changes over time > new medicines, improved
medicines, better health care
Locations and place
Location might not mean anything (coordinates)
Place means more to you > u give a meaning to that place (where
you live, where you work etc)
Places could be bad for health: pollution, disasters (Chernobyl > biggest
nuclear disaster with long lasting impact; not just a location but a place
where disaster happened)
Places can also be good for health > it can be healing (can be socially
constructed) > example hot spring > healing properties.
Key concept geography:
Distance > most fundamental concept in geography. Euclidean
distance, physical distance and cognitive distance (I think I is a 30
minute walk for me)
Scale > key concept: spatial scales (city, land etc), but you also have
different scales like temporarily scale
Ecological fallacy and modifiable areal unit problem (comes in the
exam)
Time > also key concept
Ecological model of active living
People are being influenced by their surroundings
Planning policies
Lecture 2 thinking spatially about health and methods
Wheel of research focus; how do we do research > this is the basis
structure for every research
Start with theory and questions; ideas from previous idead >
generating hypothesis.
Trying to create the setting for studying the questions and
hypothesis. Ow can u design the setting for researching. The design
of the process of thinking. What can we measure and what do we
want to measure
Collecting data (interview, secondary data etc). Analysis the
collective data
, Generalization of the findings, we found some relationships and we
are trying to generalize them. We want to understand how this
hypothesis if this is applicable for everyone or just the population
you are studying. Trees are good for people but for some they cause
allergies > not the same for everyone
Make policies that are adaptive
Why and how are these relationships happening (how do we know this
effects some people in some regio).
Qualitive methods > tells u why this happens; interviews, focus
groups, document review
Quantitative methods > focus on numbers; statistical relationships;
we apply statistics on them. Spatial models is also quantitative
Mixed methods; why is it important to have both (exam question)
why is it important for health
Health geography
Relationship between this and that in geographic context.
Looking at trees > reduce anxiety
But why might it differ how people feel? There are also different
factors, not just the trees
In the real world things can get more complex
Primary data; form a individual person about there health outcome
(interview; how do you feel about trees in your area and how are u
feeling now). Could be hard to gather this information because
privacy and shearing the information
Secondary data: aggregated > we gather data and aggregate it to a
different scale, like in neighbourhood level. Collected by individual
data and aggregated to bigger groups.
Direct primary; observed measured > we have sensors to collect
data for like air pollution. This campus has this much pollution or
noise
Air pollution exposure is more harmful for people living in deprived areas >
nearby highways (more pollution), no green spaces nearby
Quantitative data and methods
Primary data:
household surveys
blood tests, scanning,
questionnaire about physical activity.
, Use of fitness trackers; u can look at your steps and heartrate >
could be used for data collection; it is very privacy sensitive
Secondary data
CBS
Maps of loneliness, diseases
Lonleyness map: the map is very generalized, not much different
categories, the other map is way more specific and has more
information
Environmental data; loneliness could be a function of not a very
good walking neighbourhood, people don’t talk to each other
because they are only in their car. We can collect this data by
primary observations. U send a person in a neighborhood an they
collect how many cars they see, how many trees etc. it takes a lot of
time
Produced data that is secondary: pollution map of the Netherlands;
traport is seen in the map;
Tree map: all available secondary data
Micro data (secondary): footpaths width, and pollution map Utrecht
Why different kind of boundaries might cause different maps
Individual data are aggregated
Obesity exxamples; two maps; what are the differencecs > how are
the boundaries defined?
Challenges to aggregate health and contextual data in diffren geographical
units
Modifiable areal unit problem > 3 semple points and different
boundaries, u can move the boundaries, which have different
outcomes. It’s a bias about the boundary that we create. This is the
problem with modifiable areas.
Because of gerrymandering (playing with the boundaries to create a
certain outcome) > you get this problem
Mathematically there would be more problems (most important slide of the
lecture)
Scale effect > different scales, we are changing the geography of
scale (the scale is increasing and u get diffretn kind of avarages) the
bigger the scale the less variation. Tall people and long people in the
class > the avarege isn’t a good representation. Overall average
becomes the same, but the variation is less
Lecture 1 introduction
What is health > definitions
Not just healthy but also responsibilities and demands (definition 2005)
Objective measure or subjective measure
Can be individual or in population (like a population condition)
Historic background:
John snow: studied cholera outbreak > spatial mapping for health
Health:
Illness is subjective experience; your feeling tired, sad etc. but that’s
not diagnosed
Illness can become a disease if its related to specific symptoms
Sickness
Epidemiology:
Basic science of public health
Why is health geography different from epidemiology (test question)
Focuses on distribution, determinats, specified populations
Why do people in this area have different problems than in other
areas
Incidence and plrevalence
How many new people get the number of a new disease (covid >
how many new people get infected)
Prevalence
Morbidity and mortality
Mortality Number of people dying per population
Morbidity
Chronic and acute
Chronic problem: long lasting health problems (health diseases,
diabetes)
, Acute: more abrupt, like a heart attack, u need treatment
immediately
Diseases and causes changes over time > new medicines, improved
medicines, better health care
Locations and place
Location might not mean anything (coordinates)
Place means more to you > u give a meaning to that place (where
you live, where you work etc)
Places could be bad for health: pollution, disasters (Chernobyl > biggest
nuclear disaster with long lasting impact; not just a location but a place
where disaster happened)
Places can also be good for health > it can be healing (can be socially
constructed) > example hot spring > healing properties.
Key concept geography:
Distance > most fundamental concept in geography. Euclidean
distance, physical distance and cognitive distance (I think I is a 30
minute walk for me)
Scale > key concept: spatial scales (city, land etc), but you also have
different scales like temporarily scale
Ecological fallacy and modifiable areal unit problem (comes in the
exam)
Time > also key concept
Ecological model of active living
People are being influenced by their surroundings
Planning policies
Lecture 2 thinking spatially about health and methods
Wheel of research focus; how do we do research > this is the basis
structure for every research
Start with theory and questions; ideas from previous idead >
generating hypothesis.
Trying to create the setting for studying the questions and
hypothesis. Ow can u design the setting for researching. The design
of the process of thinking. What can we measure and what do we
want to measure
Collecting data (interview, secondary data etc). Analysis the
collective data
, Generalization of the findings, we found some relationships and we
are trying to generalize them. We want to understand how this
hypothesis if this is applicable for everyone or just the population
you are studying. Trees are good for people but for some they cause
allergies > not the same for everyone
Make policies that are adaptive
Why and how are these relationships happening (how do we know this
effects some people in some regio).
Qualitive methods > tells u why this happens; interviews, focus
groups, document review
Quantitative methods > focus on numbers; statistical relationships;
we apply statistics on them. Spatial models is also quantitative
Mixed methods; why is it important to have both (exam question)
why is it important for health
Health geography
Relationship between this and that in geographic context.
Looking at trees > reduce anxiety
But why might it differ how people feel? There are also different
factors, not just the trees
In the real world things can get more complex
Primary data; form a individual person about there health outcome
(interview; how do you feel about trees in your area and how are u
feeling now). Could be hard to gather this information because
privacy and shearing the information
Secondary data: aggregated > we gather data and aggregate it to a
different scale, like in neighbourhood level. Collected by individual
data and aggregated to bigger groups.
Direct primary; observed measured > we have sensors to collect
data for like air pollution. This campus has this much pollution or
noise
Air pollution exposure is more harmful for people living in deprived areas >
nearby highways (more pollution), no green spaces nearby
Quantitative data and methods
Primary data:
household surveys
blood tests, scanning,
questionnaire about physical activity.
, Use of fitness trackers; u can look at your steps and heartrate >
could be used for data collection; it is very privacy sensitive
Secondary data
CBS
Maps of loneliness, diseases
Lonleyness map: the map is very generalized, not much different
categories, the other map is way more specific and has more
information
Environmental data; loneliness could be a function of not a very
good walking neighbourhood, people don’t talk to each other
because they are only in their car. We can collect this data by
primary observations. U send a person in a neighborhood an they
collect how many cars they see, how many trees etc. it takes a lot of
time
Produced data that is secondary: pollution map of the Netherlands;
traport is seen in the map;
Tree map: all available secondary data
Micro data (secondary): footpaths width, and pollution map Utrecht
Why different kind of boundaries might cause different maps
Individual data are aggregated
Obesity exxamples; two maps; what are the differencecs > how are
the boundaries defined?
Challenges to aggregate health and contextual data in diffren geographical
units
Modifiable areal unit problem > 3 semple points and different
boundaries, u can move the boundaries, which have different
outcomes. It’s a bias about the boundary that we create. This is the
problem with modifiable areas.
Because of gerrymandering (playing with the boundaries to create a
certain outcome) > you get this problem
Mathematically there would be more problems (most important slide of the
lecture)
Scale effect > different scales, we are changing the geography of
scale (the scale is increasing and u get diffretn kind of avarages) the
bigger the scale the less variation. Tall people and long people in the
class > the avarege isn’t a good representation. Overall average
becomes the same, but the variation is less
