Spatial Thinking - Versionsgeschichte

Stefan am 19. Juni 2024 um 08:01 Uhr

2024-06-19T08:01:11Z

← Nächstältere Version		Version vom 19. Juni 2024, 08:01 Uhr
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	Note also the four principles from Peter Jackson (2006): '''1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking'''.		Note also the four principles from Peter Jackson (2006): '''1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking'''.

	Finally, there are also common stumbling blocks, like [https://macwright.com/lonlat/ "lon lat or lat lon?" by Tom McWright].		Finally, there are also common stumbling blocks, like
			* Mixing up lat/lon numbers in software tools: [https://macwright.com/lonlat/ "lon lat or lat lon?" by Tom McWright].
			* Use of non-projected data (lat/lon) for analysis, which results in distances being measured incorrectly, among other things.
			* Use of shapefiles that do not store NULL values and therefore use 0 or large negative values instead, leading to unexpected results.

	References:		References:

Stefan am 14. Juni 2024 um 10:22 Uhr

2024-06-14T10:22:08Z

← Nächstältere Version		Version vom 14. Juni 2024, 10:22 Uhr
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	In a conference, Robert Weibel (Weibel 2012) proposed to discuss and compile a set of principles and theses coming from GIS Science which could be of interest to a broader audience.		In a conference, Robert Weibel (Weibel 2012) proposed to discuss and compile a set of principles and theses coming from GIS Science which could be of interest to a broader audience.

	Hereafter there is a incomplete compilation of such '''principles~~, theses~~ and ~~problems~~ of Spatial Thinking''':		Hereafter there is a incomplete compilation of such '''principles and rules of Spatial Thinking''':

	# '''Layer principle''' - georeferenced, sharing same CRS, being able to do "spatial joins" and spatial operations (like "is within") and calculations (like "intersects/overlays").		# '''Layer principle''' - georeferenced, sharing same CRS, being able to do "spatial joins" and spatial operations (like "is within") and calculations (like "intersects/overlays").
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	Note also the four principles from Peter Jackson (2006): '''1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking'''.		Note also the four principles from Peter Jackson (2006): '''1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking'''.

			Finally, there are also common stumbling blocks, like [https://macwright.com/lonlat/ "lon lat or lat lon?" by Tom McWright].

	References:		References:

Stefan am 18. September 2023 um 14:14 Uhr

2023-09-18T14:14:15Z

← Nächstältere Version		Version vom 18. September 2023, 14:14 Uhr
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	# '''Layer principle''' - georeferenced, sharing same CRS, being able to do "spatial joins" and spatial operations (like "is within") and calculations (like "intersects/overlays").		# '''Layer principle''' - georeferenced, sharing same CRS, being able to do "spatial joins" and spatial operations (like "is within") and calculations (like "intersects/overlays").
	# '''View principle''' - Separation of data and graphic visualization.		# '''View principle''' - Separation of data and graphic visualization (aka HTML=>CSS, Markup Language=>Renderer).
	# '''GIS zoom''' - GIS zoom versus graphic zoom.		# '''GIS zoom''' - GIS zoom versus graphic zoom.
	# '''"Geographic modeling and representation problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.		# '''"Geographic modeling and representation problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.

Stefan am 19. November 2022 um 18:34 Uhr

2022-11-19T18:34:49Z

← Nächstältere Version		Version vom 19. November 2022, 18:34 Uhr
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	# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?		# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?

	Note also the four principles from Peter Jackson (2006): 1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking.		Note also the four principles from Peter Jackson (2006): '''1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking'''.

	References:		References:

Stefan: Peter Jackson

2022-11-19T18:34:29Z

Peter Jackson

← Nächstältere Version		Version vom 19. November 2022, 18:34 Uhr
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	# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)		# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)
	# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?		# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?

			Note also the four principles from Peter Jackson (2006): 1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking.

	References:		References:
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	* Openshaw, S. (1984): "The modifiable areal unit problem", In: Concepts and Techniques in Modern Geography 38: 41., see also http://www.jratcliffe.net/research/maup.htm		* Openshaw, S. (1984): "The modifiable areal unit problem", In: Concepts and Techniques in Modern Geography 38: 41., see also http://www.jratcliffe.net/research/maup.htm
	* Goodchild, M. (2004): "Spatial Thinking..." (p.53 and 54 ). http://www.csiss.org/SPACE/workshops/2004/SAG/files/goodchild_spatial.pdf		* Goodchild, M. (2004): "Spatial Thinking..." (p.53 and 54 ). http://www.csiss.org/SPACE/workshops/2004/SAG/files/goodchild_spatial.pdf
	* Tobler, W.R. (1970): A computer movie simulating urban growth in the Detroit region. Economic ~~Geography46~~: 234-240		* Tobler, W.R. (1970): "A computer movie simulating urban growth in the Detroit region". In: Economic Geography 46: 234-240
	* Jackson, ~~Peter~~ (2006): "Thinking Geographically" https://people.uwec.edu/kaldjian/1Courses/GEOG401/401Readings/Thinking_Geographically_Jackson_2006.pdf ~~mit 1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking~~		* Jackson, P. (2006): "Thinking Geographically". https://people.uwec.edu/kaldjian/1Courses/GEOG401/401Readings/Thinking_Geographically_Jackson_2006.pdf


	~~See also: [[Geovisualisierung]]~~

Stefan: Jackson, Peter (2006): "Thinking Geographically"

2022-11-18T01:12:24Z

Jackson, Peter (2006): "Thinking Geographically"

← Nächstältere Version		Version vom 18. November 2022, 01:12 Uhr
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	* Goodchild, M. (2004): "Spatial Thinking..." (p.53 and 54 ). http://www.csiss.org/SPACE/workshops/2004/SAG/files/goodchild_spatial.pdf		* Goodchild, M. (2004): "Spatial Thinking..." (p.53 and 54 ). http://www.csiss.org/SPACE/workshops/2004/SAG/files/goodchild_spatial.pdf
	* Tobler, W.R. (1970): A computer movie simulating urban growth in the Detroit region. Economic Geography46: 234-240		* Tobler, W.R. (1970): A computer movie simulating urban growth in the Detroit region. Economic Geography46: 234-240
			* Jackson, Peter (2006): "Thinking Geographically" https://people.uwec.edu/kaldjian/1Courses/GEOG401/401Readings/Thinking_Geographically_Jackson_2006.pdf mit 1. Space & Place, 2. Scale & Connection, 3. Proximity & Distance, 4. Relational Thinking


	See also: [[Geovisualisierung]]		See also: [[Geovisualisierung]]

Stefan: Homomorphic data to graphics mapping problem

2020-12-23T20:56:00Z

Homomorphic data to graphics mapping problem

← Nächstältere Version		Version vom 23. Dezember 2020, 20:56 Uhr
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	# '''"Geographic modeling and representation problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.		# '''"Geographic modeling and representation problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
	# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).		# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).
	# '''"Homomorphic mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.		# '''"Homomorphic data to graphics mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.
	# '''"Data capturing dilemma"''' - Do not capture data for the map, but for the spatial database. People tend to map the visual but should map the abstract representation. And people tend to believe the satellite map instead of accurrate vector map.		# '''"Data capturing dilemma"''' - Do not capture data for the map, but for the spatial database. People tend to map the visual but should map the abstract representation. And people tend to believe the satellite map instead of accurrate vector map.
	# '''"Limited map space conflict"''' - "All important places are at the corners of four map sheets." (Goodchild 2004)		# '''"Limited map space conflict"''' - "All important places are at the corners of four map sheets." (Goodchild 2004)

Stefan am 4. März 2020 um 16:23 Uhr

2020-03-04T16:23:58Z

← Nächstältere Version		Version vom 4. März 2020, 16:23 Uhr
Zeile 6:		Zeile 6:
	# '''View principle''' - Separation of data and graphic visualization.		# '''View principle''' - Separation of data and graphic visualization.
	# '''GIS zoom''' - GIS zoom versus graphic zoom.		# '''GIS zoom''' - GIS zoom versus graphic zoom.
	# '''"Geographic modeling problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.		# '''"Geographic modeling and representation problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
	# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).		# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).
	# '''"Homomorphic mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.		# '''"Homomorphic mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.

Stefan am 4. März 2020 um 16:23 Uhr

2020-03-04T16:23:17Z

← Nächstältere Version		Version vom 4. März 2020, 16:23 Uhr
Zeile 6:		Zeile 6:
	# '''View principle''' - Separation of data and graphic visualization.		# '''View principle''' - Separation of data and graphic visualization.
	# '''GIS zoom''' - GIS zoom versus graphic zoom.		# '''GIS zoom''' - GIS zoom versus graphic zoom.
	# '''"Geographic modeling problem"''' - How to model and encode object and field entities? As vector or raster or...?		# '''"Geographic modeling problem"''' - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
	# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).		# '''"Problem of absolute values in areal/linear features"''' - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).
	# '''"Homomorphic mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.		# '''"Homomorphic mapping problem"''' - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.
Zeile 14:		Zeile 14:
	# '''"Modifiable Areal Unit Problem"''' - (MAUP) - or "Zone Definition problem" (ZDP). A potential source of error that can affect spatial studies which utilise aggregate data sources into higher level units and scales (Ratcliffe)		# '''"Modifiable Areal Unit Problem"''' - (MAUP) - or "Zone Definition problem" (ZDP). A potential source of error that can affect spatial studies which utilise aggregate data sources into higher level units and scales (Ratcliffe)
	# '''"Tobler's first law"''' - "Nearby things are more related than distant things", Spatial Autocorrelation (Tobler 1970). Variation: "First Law of Cognitive Geography": "People think closer things are more similar" (Montello and Sarah Fabrikant)		# '''"Tobler's first law"''' - "Nearby things are more related than distant things", Spatial Autocorrelation (Tobler 1970). Variation: "First Law of Cognitive Geography": "People think closer things are more similar" (Montello and Sarah Fabrikant)
	# '''"Representation problem of continuous fields"''': While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
	# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)		# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)
	# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?		# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?

Stefan am 1. März 2020 um 16:46 Uhr

2020-03-01T16:46:10Z

← Nächstältere Version		Version vom 1. März 2020, 16:46 Uhr
Zeile 14:		Zeile 14:
	# '''"Modifiable Areal Unit Problem"''' - (MAUP) - or "Zone Definition problem" (ZDP). A potential source of error that can affect spatial studies which utilise aggregate data sources into higher level units and scales (Ratcliffe)		# '''"Modifiable Areal Unit Problem"''' - (MAUP) - or "Zone Definition problem" (ZDP). A potential source of error that can affect spatial studies which utilise aggregate data sources into higher level units and scales (Ratcliffe)
	# '''"Tobler's first law"''' - "Nearby things are more related than distant things", Spatial Autocorrelation (Tobler 1970). Variation: "First Law of Cognitive Geography": "People think closer things are more similar" (Montello and Sarah Fabrikant)		# '''"Tobler's first law"''' - "Nearby things are more related than distant things", Spatial Autocorrelation (Tobler 1970). Variation: "First Law of Cognitive Geography": "People think closer things are more similar" (Montello and Sarah Fabrikant)
	# '''"Representation problem of continuous fields"''': While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular ~~quadrilaterale~~ meshes, in addition to raster data.		# '''"Representation problem of continuous fields"''': While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
	# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)		# '''"Spatial heterogeneity problem"''' - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)
	# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?		# '''"The Data Engineering & Analytics Challenge"''' - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?