使用R进行空间微观模拟：人口综合与地理数据分析

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"Spatial.Microsimulation.with.R.14987115" 《Spatial Microsimulation with R》是一本详细介绍如何使用R语言进行空间微观模拟的书籍，它旨在帮助读者理解和实施个体层面数据的空间分配与分析。空间微观模拟是一种在地理区域中生成和分析个体数据的方法，尤其适用于健康、经济学、交通、地理等领域，需要处理小地理区域的个体级数据。书中首先介绍了空间微观模拟的基本概念，通过一个名为SimpleWorld的实例展示了这一过程。接着深入探讨了数据准备、人口合成、替代方法、实际应用、模型检查与评估等方面。对于没有微观数据的情况，书中也提供了人口合成的解决方案。此外，书中还涉及了家庭分配问题，这是理解复杂行为的关键。在第二部分，作者详细讲解了生成空间微观数据的方法，包括使用ipfp和mipfp等R包进行数据合成。这些工具允许在保持空间分辨率的同时，提供个人级别的详细信息，解决了数据保密性和可复制性的问题。第三部分则围绕着如何利用空间微观数据进行建模展开，介绍了TRESIS方法和将空间微观模拟应用于agent-based模型的策略。这部分内容有助于读者理解如何通过模拟来研究空间上变化的现象，从而提供基于证据的政策指导。附录部分，作者提供了快速掌握R语言的指南，以帮助初学者或不熟悉R的读者能够更好地跟上书中的示例和代码。这本书适合R编程的学习者和应用研究人员，他们可以通过书中丰富的可复现示例和数据，提升对复杂系统理解的能力，并运用这些技术为政策制定提供依据。无论是在学术研究还是实际工作场景，这本书都是一份宝贵的参考资料。

Acknowledgements

As with any worthwhile textbook, this was not a solo eﬀort. We beneﬁted

immensely from teaching spatial microsimulation to diverse audiences, the

formal and informal feedback they provided, and correspondence with a number

of people using spatial microsimulation ‘in the wild’. Of these, the following

deserve special mention:

•

James Gleeson, from the Greater London Authority (GLA), provided

insight into how spatial microsimulation can be used in local government

and made several improvements to the book.

•

Ulrike Rauer, from the University of Oxford, commented on early drafts of

the book and showed how it could be made more relevant to PhD students

new to the approach.

•

Stephen Clarke at the University of Leeds demonstrated the beneﬁts

of the Flexible Modelling Framework and encouraged testing of the R

code on much larger datasets than had previously been used, encouraging

optimisation of the code.

•

Johan Barthélemy, from the SMART Infrastructure (Wollongong), helped

in understanding his methods and R package (mipfp).

•

Lex Comber, who provided crucial comments on the structure of the ﬁrst

part of the book and a great insight into how to make it more useful for

teaching.

•

Malcolm Campbell, my predecessor in the PhD. Malcolm provided a huge

amount of support during the early phase of my PhD and shared all the

R code he developed. He’s been a great support of the book from the

beginning.

•

Everyone who provided input from the University of Leeds, including Mark

Birkin, Nick Malleson and Andy Evans.

Morgane Dumont’s research is funded by the Wallonia Region (Belgium) and

she is a member of NaXys (University of Namur). She thanks these two

aﬃliations in particular. We also thank the FNRS (Belgium) for the funding

that allowed Morgane to meet Robin in Leeds in 2015 to work together on the

xviii List of Figures

7.1

Example of raw aggregate level input data for CakeMap aggre-

gate data, downloaded from http://infuse.mimas.ac.uk/. . 127

7.2

Scatter plot of the relationship between observed cell counts

for all categories and all zones from the census (x axis) and

the simulated cell counts after IPF (y axis). . . . . . . . . . 130

7.3

Scatter plot of the relationship between observed cell counts

for all categories and all zones from the census (x axis) and

the simulated cell counts after IPF (y axis). . . . . . . . . . . 137

7.4

Scatter plot of the relationship between observed cell counts

for all categories and all zones from the census (x axis) and

the simulated cell counts after IPF (y axis) (ipfp vs mipfp). 139

7.5

Time necessary to perform the generation of the weight matrix

depending on the number of individuals inside the microdata. 141

8.1

Geographical distribution of Total Absolute Error (TAE) and

Relative Error (RE). . . . . . . . . . . . . . . . . . . . . . . 155

10.1 Constituent members selection process . . . . . . . . . . . . 173

10.2

Illustration of the problem of grouping members of married

couples with children. . . . . . . . . . . . . . . . . . . . . . . 176

10.3 Illustration of the algorithm to form the couples . . . . . . . 178

10.4 Illustration of the results for the couples in Belgium . . . . . 179

10.5 Illustration of the non-assigned individuals in Belgium . . . 180

10.6

Illustration of the individuals in a household of a diﬀerent size

for Belgium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

11.1 Overview of TRESIS model structure . . . . . . . . . . . . . . 187

12.1 Setting the world size and resolution . . . . . . . . . . . . . . 211

12.2 SimpleWorld in NetLogo . . . . . . . . . . . . . . . . . . . . 212

12.3 SimpleWorld in NetLogo . . . . . . . . . . . . . . . . . . . . 214

12.4 SimpleWorld populated with 33 inhabitants . . . . . . . . . 218

12.5 New global variables deﬁned using sliders . . . . . . . . . . . 220

12.6 SimpleWorld in negotiations . . . . . . . . . . . . . . . . . . 225

12.7 Plotting SimpleWorld . . . . . . . . . . . . . . . . . . . . . . 229

12.8 NetLogo data plotted in R . . . . . . . . . . . . . . . . . . . 233

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使用R进行空间微观模拟：人口综合与地理数据分析

3D-mathnet-spatial.zip

oracle.spatial的jar包

怎么下载这个库scipy.spatial.transform

scipy.spatial.alpha_shape

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from scipy.spatial.distance import euclidean

from scipy.spatial.distance import cdist

ModuleNotFoundError: No module named 'scipy.spatial.transform'

ModuleNotFoundError: No module named 'scipy.spatial.transform

Coding me scipy.spatial.Delaunay create mesh

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