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--- geopro:raian:bibliografia:neighborhoods [2010/03/05 20:03] – raian
+++ geopro:raian:bibliografia:neighborhoods [2011/08/29 22:19] (atual) – raian
@@ Linha 109: / Linha 109: @@
       - Determine to what extent land-use related net neighborhood influences explain past land-use changes and can be used to simulate future changes. Neighborhood rules derived from the first part is assessed by calibration and validation of the CA land-use model for a number of urban regions. Compare, based on it, the survey-based rules and the calibration-based rules, making it possible to assess their values for future CA modelling.
     - **Spatial externalities and urban dynamics:**
-      * Urban dynamics are closely related to the widely accepted to the first law of geography, formulated by Tobler in 1970: "Everything is related to everythig else, but near things are more related than distant things". Many works have stressed the importace of the concept of "situation", the relative location of a site.
+      * Urban dynamics are closely related to the widely accepted first law of geography, formulated by Tobler in 1970: "Everything is related to everythig else, but near things are more related than distant things". Many works have stressed the importace of the concept of "situation", the relative location of a site.
+      * The land-use of any parcel is affected more by the land-use of surrounding parcels (situational factors) than by what takes place on the parcel itself, the so-called site-factors (Clawson, 1971).
       * The externalities, as defined in the paper (see page 41) are seen as the organising forces of urban patterns. Every activity of an element in an urban system might generate unpriced and perhaps non-monetary effects upon other elements.
       * Most external effects are constrained to a spatially limited area, "spatial externalities". The definition of spatial externalities used is: the radiated priced or unpriced effects of one land-use on another land-use. It can be envisioned as a form of __situational factors__.
+      * Every activity of an element in an urban system might generate unpriced and perhaps non-monetary effects upon other elements (Harvey, 1971, 1973).
+      * Spatial externalities can be envisioned as the unpriced effects of a certain activity  upon individuals who are not directly involved in that activity (Dear, 1977).
+      * In general, "spatial externalities" are things that are out of the studied phenomenon and interfere in it. In the paper, the author gives definitions made by several other authors.
+      * It have a great impact on urban structure.
+      * The diminishing effect of distance on the intensity of spatial interactions, also called distance-decay, either is considered. It happen because the distance creates friction, there tends to be a systematic decline in the interaction. So, three dimensions can be distinguished for all land-use interactions: //intensity//, //extent//, and //distance-decay//.
+        - //Intensity// -> the impact of the external effect.
+        - //Extent// -> the absolute geographical area covered by the external effect.
+        - //Distance-decay// -> the manner in which it dissipates over space.
+      * Among the many methods to model land-use change, optimisation models and linear programming, dynamic simulation models, agent-based models and Cellular Automata (CA) models are the most important. The CA models indicates how urban structure changes incrementally over time. They are very suitable for representing the dispersal of activities and characteristics between discrete spatial units of urban infrastructure, and for defining the spatial interactions between land-uses at certain locations, the conditions at these locations and the land-use types in the surrounding environment.
+      * Many studies have indicated that the validity of neighborhood rules is an urgent research issue. Various attempts have been undertaken to define neighborhood rules more fundamentally, but they have not proved to be very satisfactory.
+      * **Problem:** to be able to justify the application of CA land-use models for policy support, the underlying neighborhood rules need a better foundation.
+    - **Methodology:**
+      * 15 urban land-use categories were used.
+      * Were used raster maps with 100m grid cells.
+      * In theory, the 15 land-uses imply 225 neighborhood rules, each possibly based on multiple externality effects playng over different distances.
+      * Because of the great contribution of intangibles to externality effects, it is virtually impossible to evaluate every effect in absolute comparable units.
+      * The method has led to uncertain results, characteristic of much of the empirical research relating to the net impact of land-uses and neighborhood rules.
+      * The number of land-use relations to be analysed can be greatly reduced without compromising the result to an unacceptable level.
+        - The spatial influence of all land-uses on the static infrastructural land-uses, of which change is generally not modelled but imposed externally, does not have to be taken into account.
+        - In practice a number of land-uses do not interact and are hardly ever relevant. Per modelled land-use category, two or three combinations are most important and already sufficient for analysis.
+        - The coalescence of two externality effects from noe land-use is less common than the coalescence of single externality effects from a large number of land-uses. In another words, of all the externalities caused and experienced by a land-use, only a very selective number play a dominant role in the interaction between two land-uses.
+        - All the externality effects are constrained to a spatially limited area within which they are more dominant at certain distances than others. There tends to be a systematic decline in the externality effect over distance, and such distance-decay effects have to be observed empirically in many cases.
+        - From all possible distance functions, six different general shapes can be deduced, identifying how an externality effect can change over distance. These functions are shown bellow.
+        - It is not necessary to know the value at every distance to determine the shape of a neighborhood rule. For each effect, a maximum of five distance points is needed closely match the valid effect.
+            * {{:geopro:raian:bibliografia:distance_functions.png?500}}
+      * The authors made interviews with experts in different areas of study of urban relations to reach neighborhood rules and distance functions. These rules are more complex than general linear distance functions, and consequently more realistic.
+      * The composition and pattens described by spatial metrics can be of great importance in economic geography. Spatial metrics are more capable of quantifying different dimensions of the urban morphological structure and "spatial signatures" of land-uses than the more fragmented pattern measures used before. Structure refers to the distribution of land-uses in relation to their size, shapes, numbers, kinds and configurations of the urban system.
+      * The so-called "enrichment factor", is a useful empirical method for analysing interaction between neighboring land-uses or neighborhood characteristics. It measures over- or under-representation of different land-use types in a specific neighborhood of a location by comparing the occurrence of a land-use type in that neighborhood of the location relative to the occurrence of this land-use type in the study area as a whole. It can be used to test and validate any hypothesis concerning neighborhood interactions.
+      * When calculated for different regions through time, the enrichment factor can also give insights into the temporal dynamics of the neighborhood interactions and structures and regional differences herein. It was calculated for the time series of land-use patterns of the four urban regions studied. The outcomes of these analyses contributed to the new sets of neighborhood rules for each of the regions.
+      * It was applied in a series of CA applications, which was run for two historic periods (1986-1993 and 1993-2000) for calibration and validation, and than to on prospective analyses (2000-2014). Fuzzy Kappa statistics and a Random Constraints Match was used to test the explanatory power.