Muutke küpsiste eelistusi

E-raamat: Exploring Spatial Scale in Geography

4.00/5 (2 hinnangut Goodreads-ist)
(University of Liverpool, UK)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 24-Feb-2014
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118526798
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 113,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Exploring Spatial Scale in GeographySuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 24-Feb-2014
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118526798
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Exploring Spatial Scale in Geography provides a conceptual and practical guide to issues of spatial scale in all areas of the physical and social sciences. Scale is at the heart of geography and other spatial disciplines; whether dealing with geomorphological processes, population movements or meteorology, a consideration of spatial scale is vital.

This book offers alternative definitions of spatial scale, presents approaches for exploring spatial scale, and makes use of a wide variety of case studies in the physical and social sciences to demonstrate key concepts. It consists of three integrated strands. The first is conceptual, outlining some definitions of spatial scale and debating the meaning and value of concepts of scale. The second strand outlines methods for exploration of spatial scale including standard measures of spatial autocorrelation, fractals, wavelets, multilevel models and geostatistical measures. The third and final strand demonstrates the application of these concepts and methods to real world case studies.

The author takes a practical approach and links are made to appropriate software environments, with an associated website providing access to guidance material which outlines how particular problems can be approached using popular GIS and spatial data analysis software. As such, it is a key resource for GIS professionals, academics and postgraduate students in geography, environmental science, civil and environmental engineering, remote sensing, archaeology and related subjects.

Exploring Spatial Scale in Geography provides a conceptual and practical guide to issues of spatial scale in all areas of the physical and social sciences. Scale is at the heart of geography and other spatial sciences.

Exploring Spatial Scale in Geography provides a conceptual and practical guide to issues of spatial scale in all areas of the physical and social sciences. Scale is at the heart of geography and other spatial sciences. Whether dealing with geomorphological processes, population movements or meteorology, a consideration of spatial scale is vital.

Exploring Spatial Scale in Geography takes a practical approach with a core focus on real world problems and potential solutions. Links are made to appropriate software environments with an associated website providing access to guidance material which outlines how particular problems can be approached using popular GIS and spatial data analysis software.

This book offers alternative definitions of spatial scale, presents approaches for exploring spatial scale and makes use of a wide variety of case studies in the physical and social sciences to demonstrate key concepts, making it a key resource for anyone who makes use of geographical information.

Arvustused

"The type of reader who would likely gain from the book is from the last group, the researcher seeking an annotated bibliography. Each chapter ends with suggestions for further reading. Extensive references are cited at the back of eachchapter rather than at the end of the book. The index is quite valuable and was accurate for all the terms I checked." Mathematical Geosciences, 2015

"This book provides a systematic and comprehensive account of the many spatial analytic methods useful for understanding the implications of spatial scale for geographical data. The author effectively and concisely covers a wide range of methods, using a variety of different data sets and examples." International Journal of Geographical Information Science, 2015

Preface xiii
Acknowledgements xv
About the Companion Website xvii
1 Introduction 1(8)
1.1 The purpose of the book
1(3)
1.1.1 What this book adds
3(1)
1.1.2 Scales of analysis and alternative definitions
3(1)
1.2 Key objectives
4(1)
1.3 Case studies and examples
5(1)
1.4 Why is spatial scale important?
5(1)
1.5 Structure of the book
6(1)
1.6 Further reading
6(1)
References
7(2)
2 Scale in Spatial Data Analysis: Key Concepts 9(20)
2.1 Definitions of spatial scale
9(2)
2.2 Spatial autocorrelation and spatial dependence
11(2)
2.3 Scale dependence
13(1)
2.4 Scale and data models
14(1)
2.5 Spatial scales of inquiry
14(1)
2.6 Scale and spatial data analysis
14(1)
2.7 Scale and neighbourhoods
15(1)
2.8 Scale and space
16(7)
2.9 Scale, spatial data analysis and physical processes
23(2)
2.10 Scale, spatial data analysis and social processes
25(1)
2.11 Summary
26(1)
2.12 Further reading
26(1)
References
26(3)
3 The Modifiable Areal Unit Problem 29(16)
3.1 Basic concepts
29(1)
3.2 Scale and zonation effects
29(3)
3.3 The ecological fallacy
32(2)
3.4 The MAUP and univariate statistics
34(4)
3.4.1 Case study: segregation in Northern Ireland
35(3)
3.4.2 Spatial approaches to segregation
38(1)
3.5 Geographical weighting and the MAUP
38(1)
3.6 The MAUP and multivariate statistics
39(2)
3.6.1 Case study: population variables in Northern Ireland
40(1)
3.7 Zone design
41(1)
3.8 Summary
42(1)
3.9 Further reading
42(1)
References
42(3)
4 Measuring Spatial Structure 45(58)
4.1 Basic concepts
45(1)
4.2 Measures of spatial autocorrelation
45(8)
4.2.1 Neighbourhood size
47(1)
4.2.2 Spatial autocorrelation and kernel. size
47(3)
4.2.3 Spatial autocorrelation and lags
50(1)
4.2.4 Local measures
50(1)
4.2.5 Global and local I and spatial scale
51(2)
4.3 Geostatistics and characterising spatial structure
53(4)
4.3.1 The theory of regionalised variables
54(3)
4.4 The variogram
57(2)
4.4.1 Bias in variogram estimation
59(1)
4.5 The covariance function and correlogram
59(1)
4.6 Alternative measures of spatial structure
60(3)
4.7 Measuring dependence between variables
63(1)
4.8 Variograms of risk
64(1)
4.9 Variogram clouds and h-scatterplots
64(1)
4.10 Variogram models
65(3)
4.11 Fitting variogram models
68(2)
4.12 Variogram case study
70(4)
4.13 Anisotropy and variograms
74(3)
4.13.1 Variogram surfaces
74(1)
4.13.2 Geometric and zonal anisotropy
75(2)
4.14 Variograms and non-stationarity
77(5)
4.14.1 Variograms and long-range trends
77(2)
4.14.2 Variogram non-stationarity
79(3)
4.15 Space-time variograms
82(1)
4.16 Software
83(1)
4.17 Other methods
83(1)
4.18 Point pattern analysis
84(13)
4.18.1 Spatial dependence and point patterns
85(6)
4.18.2 Local K function
91(1)
4.18.3 Cross K function
92(5)
4.19 Summary
97(1)
4.20 Further reading
97(1)
References
97(6)
5 Scale and Multivariate Data 103(32)
5.1 Regression frameworks
104(1)
5.2 Spatial scale and regression
104(1)
5.3 Global regression
105(1)
5.4 Spatial regression
105(1)
5.5 Regression and spatial data
106(5)
5.5.1 Generalised least squares
106(1)
5.5.2 Spatial autoregressive models
107(2)
5.5.3 Spatially lagged dependent variable models and spatial error models case study
109(2)
5.6 Local regression and spatial scale
111(8)
5.6.1 Spatial expansion method
111(1)
5.6.2 Geographically weighted regression
112(3)
5.6.3 Scale and GWR
115(1)
5.6.4 GWR case study: fixed bandwidths
115(1)
5.6.5 GWR case study: variable bandwidths
116(2)
5.6.6 Bayesian spatially varying coefficient process models
118(1)
5.7 Multilevel modelling
119(10)
5.7.1 Case study
125(4)
5.8 Spatial structure of multiple variables
129(1)
5.9 Multivariate analysis and spatial scale
130(1)
5.10 Summary
131(1)
5.11 Further reading
131(1)
References
131(4)
6 Fractal Analysis 135(24)
6.1 Basic concepts
135(3)
6.2 Measuring fractal dimension
138(4)
6.2.1 Walking-divider method
139(1)
6.2.2 Box-counting method
140(2)
6.2.3 Variogram method
142(1)
6.3 Fractals and spatial structure
142(10)
6.3.1 Case study: fractal D of land surfaces
143(3)
6.3.2 Case study: local fractal D
146(3)
6.3.3 Fractals and topographic form
149(3)
6.4 Other applications of fractal analysis
152(3)
6.4.1 Fractals and remotely sensed imagery
152(1)
6.4.2 Fractals and urban form
153(2)
6.5 How useful is the fractal model in geography?
155(1)
6.6 Summary
155(1)
6.7 Further reading
155(1)
References
155(4)
7 Scale and Gridded Data: Fourier and Wavelet Transforms 159(24)
7.1 Basic concepts
159(1)
7.2 Fourier transforms
160(8)
7.2.1 Continuous Fourier transform
160(1)
7.2.2 Discrete Fourier transform
161(2)
7.2.3 Fast Fourier transform
163(1)
7.2.4 FFT case study
163(2)
7.2.5 Spectral analysis and the covariance function
165(2)
7.2.6 Spectral analysis case study
167(1)
7.3 Wavelet transforms
168(12)
7.3.1 Continuous wavelet transforms
169(1)
7.3.2 Discrete wavelet transforms
170(1)
7.3.3 The Haar basis functions
171(1)
7.3.4 Other basis functions
172(1)
7.3.5 Fast wavelet transform
173(1)
7.3.6 Two-dimensional wavelet transforms
174(6)
7.4 Wavelet analysis applications and other issues
180(1)
7.5 Summary
180(1)
7.6 Further reading
180(1)
References
181(2)
8 Areal Interpolation 183(18)
8.1 Basic concepts
183(1)
8.2 Areal weighting
184(2)
8.3 Using additional data
186(7)
8.3.1 Types of secondary data sources for mapping populations
192(1)
8.4 Surface modelling
193(3)
8.4.1 Population surface case study
195(1)
8.5 Other approaches to changing support
196(1)
8.6 Summary
197(1)
8.7 Further reading
198(1)
References
198(3)
9 Geostatistical Interpolation and Change of Support 201(40)
9.1 Basic concepts
201(1)
9.2 Regularisation
201(4)
9.2.1 Regularisation with an irregular support
204(1)
9.3 Variogram deconvolution
205(5)
9.3.1 Variogram deconvolution for irregular supports
206(2)
9.3.2 Variography and change of support
208(2)
9.4 Kriging
210(16)
9.4.1 Punctual kriging
210(2)
9.4.2 Poisson kriging
212(1)
9.4.3 Factorial kriging
213(2)
9.4.4 Factorial kriging case study
215(1)
9.4.5 Kriging in the presence of a trend
215(7)
9.4.6 Cokriging
222(1)
9.4.7 Kriging with an external drift and other techniques
222(1)
9.4.8 Interpreting the kriging variance
223(1)
9.4.9 Cross-validation
223(1)
9.4.10 Conditional simulation
224(1)
9.4.11 Comparison of kriging approaches
224(2)
9.5 Kriging and change of support
226(5)
9.5.1 Block kriging
226(1)
9.5.2 Area-to-point kriging
227(2)
9.5.3 Case study
229(2)
9.6 Assessing uncertainty and optimal sampling design
231(5)
9.6.1 Nested sampling
231(1)
9.6.2 Assessing optimal sampling design
232(3)
9.6.3 Optimal spatial resolution
235(1)
9.6.4 Other approaches to optimal sampling design
236(1)
9.7 Summary
236(1)
9.8 Further reading
236(1)
References
236(5)
10 Summary and Conclusions 241(6)
10.1 Overview of key concepts and methods
241(2)
10.2 Problems and future directions
243(2)
10.3 Summary
245(1)
References
245(2)
Index 247
Christopher D. Lloyd Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, UK.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811185267982e.html
Märksõnad: