Summary

Thinking Spatially - Final Summary (based on 6 weeks)

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Course
73414B005Y

Institution
Universiteit Van Amsterdam (UvA)

This is my summary for the course "Thinking Spatially at the University of Amsterdam". It is based on readings, notes, and lectures. It provides with many insights on research methods, qualitative methods, quantitative methods, history and use of GIS, spatial analysis, and so on.

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Uploaded on April 11, 2024
Number of pages 43
Written in 2023/2024
Type Summary

space
gis
spatial analysis
research methods
qualitative analysis
quantitative analysis

Institution
Universiteit van Amsterdam (UvA)
Education
Human Geography and Urban Planning
Course
73414B005Y

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WEEK 1
Kraak - Science and Maps
History of maps: since the 1980s, digital geospatial data handling has become prominent, leading to the
development of Geographic Information Systems (GIS). GIS allows users to manipulate, analyze, and visualize
integrated geospatial data from various sources, enabling applications across different disciplines.

Today: on-screen maps and the internet have revolutionized mapping, making interactive maps accessible to a
wider audience. Google Maps/ Earth, for example, allows users to add and share their data. The convergence of
disciplines working with geo-information has given rise to Geographical Information Science (GIScience),
focusing on research both on and with GIS. Also, spatial data infrastructures (SDI) have emerged to facilitate
access, integration, and use of geospatial data worldwide. In GIScience, visualization plays a crucial role in the
exploration, analysis, synthesis of results, and communication of geospatial knowledge. Well-designed maps aid
in presenting complex analysis results to a broader audience and also provide easier access to the underlying
data files.

Four fields of visualization in Geographic Information Science (GIScience):
1. exploration.
2. analysis.
3. synthesis.
4. presentation: tools such as map production are highly developed and guided by cartographic rules.
However, these rules are not embedded in mapping software, allowing users to create maps
independently, though effectiveness is not guaranteed.

Geospatial data: geographical information pertains to objects or phenomena with specific spatial locations. This
spatial characteristic allows for visualization, primarily through maps. Objects from the real world are abstracted
into a Digital Landscape Model (DLM) based on predetermined criteria, stored in Geographic Information
Systems (GIS) as points, lines, areas, or volumes, and then represented on maps as a Digital Cartographic
Model (DCM).

Geospatial data in databases are typically divided into locational, attribute, and temporal data, answering the
elementary questions of Where?, What?, and When? Location, attribute, and time can each have multiple
characteristics, such as different coordinate systems or various variables. The questions Why? and How? require
further analysis and perspective from location space, attribute space, or time-space.

From real-world objects to digital models: when communicated to others, this DLM is converted into a DCM
with instructions for reproduction in different forms. Users then process the mapped information into their
cognitive map, influencing decision-making. For data to be considered geometric or georeferenced, information
about their location is crucial, expressed through geographical or reference grid coordinates, code numbers,
topological terms, or nominal terms like street addresses. The geospatial nature of objects is categorized into
point-, line-, area-, or volumetrically shaped objects, further subdivided based on shapes like elongated,
triangular, irregular, or convex. This categorization is scale or resolution-dependent.

Discrete or continuous objects: discrete objects have explicit coordinates, suitable for mapping bordered
entities, while continuous representations pertain to phenomena changing non-incrementally in value.

Qualitative or quantitative attributes: measurement scales such as nominal, ordinal, interval, and ratio are used
to assess attribute values. Geospatial data, including geometry, attribute values, and time stamps, are subject to
changes over time.

Visualization: is essential for studying or analyzing geospatial data, employing cartographic grammar to convey
relationships between points, lines, areas, or volumetric objects. The dimensionality of representation varies,

,ranging from 1-dimensional (e.g. distance from a central market) to 3-dimensional (e.g. contour maps) and
4-dimensional when adding the time dimension.

GIS
Evolution: initially, computers were used to create inventories of discipline-dependent data, allowing for the
production of maps that were previously made manually. The focus then shifted to spatial analysis, where
statistical methods were applied to attribute data, enabling the creation of different derived products from the
existing database. Nowadays, problems are approached in an interdisciplinary way, requiring data from various
fields. This led to the development of GIS, which integrates geospatial data sets from different sources, such as
surveys, remote sensing, statistical databases, and paper maps. GIS is now used in virtually all disciplines that
require geospatial data for their tasks or problem-solving processes.

GIS development was driven by diverse fields such as forestry, defense, cadastre, utilities, and regional
planning, each with different needs and backgrounds.

Core functionality: it comes from disciplines like geography, geodesy, and cartography, which work with spatial
data. Database technology and computer graphics are added to this core, enhancing the capabilities of GIS. GIS
is unique because it can combine geospatial and non-geospatial data from different sources in geospatial
analysis operations, allowing it to answer various questions.
1. What is there? by pointing at a location on a map and retrieving information stored on the object.
2. Where is X? by providing one or more locations adhering to specified criteria.
3. What has changed since ...?" by showing temporal trends over time.
4. What if ...? by forecasting and addressing questions related to future scenarios.

Working definition: GIS is a computer-assisted information system within an organization. Its purpose is to
collect, store, manipulate, and display spatial data for decision support.

Components: GIS consists of software, hardware, geospatial data, and organizational components. These
elements communicate through a set of procedures. The central components of GIS include (1) the
problem-solving production line (2) a geospatial analysis potential, and (3) integration of datasets. Each
organization requires a GIS with functions tailored to its specific tasks. Common functions include data input
and encoding, data manipulation, data retrieval, data presentation, and integrated data management.

Role of cartography: maps serve as a direct and interactive interface to GIS, providing a graphical user interface
with a geospatial dimension. They act as visual indexes to phenomena within information systems, aid in the
visual exploration of data sets, and facilitate visual communication of results.
1. Cartographers have extensive experience in working with different data sets and have developed
methodologies for transforming and integrating them, ensuring data quality and compatibility.
2. Cartographers have contributed to improving spatial data infrastructure, ensuring better access to
geospatial data, which is crucial for GIS applications.

,Decision-making: cartography provides essential tools for collecting, processing, analyzing, and communicating
geospatial data to decision-makers. GIS users equipped with map skills and access to quality data can facilitate
correct decision-making procedures.

Processes of GIS: the Maastricht case
Objective definition and conditions: includes specific restrictions and constraints to adhere to. In Maastricht,
municipal authorities wanted to know how municipal forests developed from 1950 to 1990. Also, they wanted a
map indicating unchanged and new forests, as well as a table with the size of forest parcels.
Data preparation: involves collecting, formatting, and integrating various datasets. This may include digitizing
paper maps, generalizing boundaries, and extracting relevant information.
Execution of geospatial analysis: It involves operations on geometric and non-geometric data components.
Three major types are overlay and buffer operations, network operations, and surface operations. In the
Maastricht case, a simple overlay operation was conducted to combine land use data from 1950 and 1990,
revealing unchanged, removed, and new forest parcels.
Statistical analysis and evaluation: Basic statistics may include calculations on parcel size or perimeter.
Evaluation and interpretation of results involve comparing expectations with actual findings. Any discrepancies
may require adjustments to the analysis process.

Possible issues related to GIS
Types of errors in geospatial data: errors can arise during data collection, digitization, or analysis operations.
These errors include measurement errors, classification errors, localization errors, and reproduction errors. The
impact of these errors on analysis results and decision-making is not always clear and requires further
investigation.

Identification of errors: despite appearing correct initially, closer inspection reveals inaccuracies, such as the
presence of "sliver polygons" in the result, which are small polygons indicating change where none actually
occurred. Such errors can propagate into future analyses if not addressed, potentially leading to unnecessary
database growth. It is crucial to ensure data quality before making decisions based on geospatial analysis results.
The suitability of data for specific applications (fitness for use) should be assessed to avoid misleading
conclusions.

Presentation of results: Geospatial analysis results are often presented in reports using maps, diagrams, and
tables to illustrate conclusions. While most GIS packages offer basic cartographic functionality, dedicated
desktop packages are better suited for producing final maps. Visualization through maps enhances
understanding and interpretation of geospatial analysis results, facilitating decision-making processes. Although
it's possible to conduct geospatial analysis without maps, visualization greatly aids in comprehension and
communication.

Importance of utilizing maps: TGV case study
TGV: Netherlands’ high-speed train operating in the Randstad area. In 1994, plans were made to extend the
Paris–Brussels TGV link to Amsterdam, necessitating the selection of a new route through the “Green Heart” of
the Randstad area in the West. The goal was to minimize environmental impact, avoiding disturbance to nesting
birds, pollution of groundwater, and damage to valuable geoscientific monuments.

EIS: to aid in route selection, an Environmental Information System (EIS) was consulted. This system contained
comprehensive data on soils, groundwater, vegetation, fauna, and geological features collected on a grid-cell
basis (1 sq km each). By analyzing factors such as soil susceptibility to water-table lowering, habitat
fragmentation for mammals, and the effects on bird life, the potential impacts of various routes were evaluated.
Utilizing computer calculations, the total environmental impact for each proposed route was determined,
allowing for the selection of the route with the least damage. This approach enabled informed decision-making
while prioritizing environmental conservation in the construction of the TGV link.

, Spatial Data Infrastructure
Companies and government departments: they operate with vast amounts of information, ranging from
inventorying objects they administer, to monitoring the state of the environment, or for crime. Often, they share
part of this information with others and require data from various organizations. Location serves as a crucial
component that links different datasets, allowing for the combination of data based on common locations like
street addresses, postal codes, or geographical coordinates.

To realize the added value of combining datasets, certain conditions must be met, including compatibility of
programs or packages used, file structures, resolution, and survey timeframe. Spatial infrastructures, guided by
the principle of “collect once, use many times”, aim to facilitate smooth data exchange. A Geographic Data
Infrastructure (GDI) enhances the availability and integration of geo-information to support decision-making
related to sustainable development. In Europe, the implementation of GDIs is driven by initiatives like
INSPIRE, emphasizing principles such as data stewardship, accessibility, interoperability, reusability,
discoverability, validity, and usability. INSPIRE follows standards established by the Open Geospatial
Consortium (OGC), which develops protocols and interfaces for geospatial and location-based services to
ensure data providers can offer their products in a standard way.

Clearinghouses: are established to help data users determine if datasets from different sources can be combined.
These platforms provide metadata, including data quality, accuracy, collection methods, and fitness for use. The
Open Geospatial standards ensure that data collected from different sources can be effectively utilized.

World Wide Web (WWW): it has become a vital medium for acquiring and disseminating geospatial data,
leading to increased involvement in mapping. This surge in mapmaking via the web raises questions about the
role of geo-professionals. With the WWW, virtually anyone with access can create maps, resulting in diverse
and interactive map products. The WWW offers several advantages for maps, including platform independence,
cost-effective reach to a large audience, easy updates, and dynamic dissemination of geospatial data. The
introduction of Google Earth and Google Maps in 2005 revolutionized map viewing by providing free access to
detailed satellite imagery and maps. This development, termed “neogeography”, allowed users to explore
Earth's surface in three dimensions and share their data on these platforms.

Clark - The Nature and Process of Social Research
Social research: encompasses academic inquiries conducted by social scientists from various disciplines, such as
sociology, anthropology, and criminology. It explores societal phenomena using the theories and methodologies
of the social sciences, aiming to generate new knowledge and deepen our understanding of contemporary social
life. Social research employs diverse methods, ranging from content analysis to surveys, to investigate topics
like media portrayal of gender or the impact of social media on crime rates.

Aim: conducting social research is essential for unraveling the complexities of society, understanding human
behavior, and addressing unanswered questions. It serves as a means to search for answers, whether prompted
by gaps in existing literature, inconsistencies in previous studies, or societal developments. Studying research
methods equips individuals with the necessary tools to investigate the social world, enabling them to contribute
new findings and insights to important societal debates and issues.

Theories: they shape research topics and influence how findings are interpreted. Researchers may engage with
theories at the beginning of a project to guide their exploration (inductive), or theories may emerge as outcomes
of the research process itself (deductive). Views on the role of theory vary among researchers.

Literature: familiarity with existing literature in the field is crucial for building upon previous work and
avoiding redundancy. A thorough literature review is essential for informing research design and methodology.

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