- Teks
- Sejarah
(i) It is much easier to move data
(i) It is much easier to move data across spatial statistics packages. The
classes are either supported directly by the packages, reading and writing
data in the new spatial classes, or indirectly, for example by supplying
data conversion between the sp classes and the package’s classes in an
interface package. This last option requires one-to-many links between the
packages, which are easier to provide and maintain than many-to-many
links.
(ii) The new classes come with a well-tested set of methods (functions) for
plotting, printing, subsetting, and summarising spatial objects, or combining
(overlaying) spatial data types.
(iii) Packages with interfaces to geographical information systems (GIS), for
reading and writing GIS file formats, and for coordinate (re)projection
code support the new classes.
(iv) The new methods include Lattice plots, conditioning plots, plot methods
that combine points, lines, polygons, and grids with map elements (reference
grids, scale bars, north arrows), degree symbols (as in 52◦N) in axis
labels, etc.
Chapter 2 introduces the classes and methods provided by sp, and discusses
some of the implementation details. Further chapters will show the degree of
integration of sp classes and methods and the packages used for statistical
analysis of spatial data.
Figure 1.1 shows how the reception of sp classes has already influenced the
landscape of contributed packages; interfacing other packages for handling and
analysing spatial data is usually simple as we see in Part II. The shaded nodes
of the dependency graph are packages (co)-written and/or maintained by the
authors of this book, and will be used extensively in the following chapters.
1.3 R and GIS
1.3.1 What is GIS?
Storage and analysis of spatial data is traditionally done in Geographical Information
Systems (GIS). According to the toolbox-based definition of Burrough
and McDonnell (1998, p. 11), a GIS is ‘...a powerful set of tools for collecting,
storing, retrieving at will, transforming, and displaying spatial data from the
real world for a particular set of purposes’. Another definition mentioned in
the same source refers to ‘...checking, manipulating, and analysing data, which
are spatially referenced to the Earth’.
Its capacity to analyse and visualise data makes R a good choice for spatial
data analysis. For some spatial analysis projects, using only R may be sufficient
for the job. In many cases, however, R will be used in conjunction with GIS
software and possibly a GIS data base as well. Chapter 4 will show how spatial
data are imported from and exported to GIS file formats. As is often the case
in applied data analysis, the real issue is not whether a given problem can be
classes are either supported directly by the packages, reading and writing
data in the new spatial classes, or indirectly, for example by supplying
data conversion between the sp classes and the package’s classes in an
interface package. This last option requires one-to-many links between the
packages, which are easier to provide and maintain than many-to-many
links.
(ii) The new classes come with a well-tested set of methods (functions) for
plotting, printing, subsetting, and summarising spatial objects, or combining
(overlaying) spatial data types.
(iii) Packages with interfaces to geographical information systems (GIS), for
reading and writing GIS file formats, and for coordinate (re)projection
code support the new classes.
(iv) The new methods include Lattice plots, conditioning plots, plot methods
that combine points, lines, polygons, and grids with map elements (reference
grids, scale bars, north arrows), degree symbols (as in 52◦N) in axis
labels, etc.
Chapter 2 introduces the classes and methods provided by sp, and discusses
some of the implementation details. Further chapters will show the degree of
integration of sp classes and methods and the packages used for statistical
analysis of spatial data.
Figure 1.1 shows how the reception of sp classes has already influenced the
landscape of contributed packages; interfacing other packages for handling and
analysing spatial data is usually simple as we see in Part II. The shaded nodes
of the dependency graph are packages (co)-written and/or maintained by the
authors of this book, and will be used extensively in the following chapters.
1.3 R and GIS
1.3.1 What is GIS?
Storage and analysis of spatial data is traditionally done in Geographical Information
Systems (GIS). According to the toolbox-based definition of Burrough
and McDonnell (1998, p. 11), a GIS is ‘...a powerful set of tools for collecting,
storing, retrieving at will, transforming, and displaying spatial data from the
real world for a particular set of purposes’. Another definition mentioned in
the same source refers to ‘...checking, manipulating, and analysing data, which
are spatially referenced to the Earth’.
Its capacity to analyse and visualise data makes R a good choice for spatial
data analysis. For some spatial analysis projects, using only R may be sufficient
for the job. In many cases, however, R will be used in conjunction with GIS
software and possibly a GIS data base as well. Chapter 4 will show how spatial
data are imported from and exported to GIS file formats. As is often the case
in applied data analysis, the real issue is not whether a given problem can be
0/5000
(i) It is much easier to move data across spatial statistics packages. Theclasses are either supported directly by the packages, reading and writingdata in the new spatial classes, or indirectly, for example by supplyingdata conversion between the sp classes and the package’s classes in aninterface package. This last option requires one-to-many links between thepackages, which are easier to provide and maintain than many-to-manylinks.(ii) The new classes come with a well-tested set of methods (functions) forplotting, printing, subsetting, and summarising spatial objects, or combining(overlaying) spatial data types.(iii) Packages with interfaces to geographical information systems (GIS), forreading and writing GIS file formats, and for coordinate (re)projectioncode support the new classes.(iv) The new methods include Lattice plots, conditioning plots, plot methodsthat combine points, lines, polygons, and grids with map elements (referencegrids, scale bars, north arrows), degree symbols (as in 52◦N) in axislabels, etc.Chapter 2 introduces the classes and methods provided by sp, and discussessome of the implementation details. Further chapters will show the degree ofintegration of sp classes and methods and the packages used for statisticalanalysis of spatial data.Figure 1.1 shows how the reception of sp classes has already influenced thelandscape of contributed packages; interfacing other packages for handling andanalysing spatial data is usually simple as we see in Part II. The shaded nodesof the dependency graph are packages (co)-written and/or maintained by theauthors of this book, and will be used extensively in the following chapters.1.3 R and GIS1.3.1 What is GIS?Storage and analysis of spatial data is traditionally done in Geographical InformationSystems (GIS). According to the toolbox-based definition of Burroughand McDonnell (1998, p. 11), a GIS is ‘...a powerful set of tools for collecting,storing, retrieving at will, transforming, and displaying spatial data from thereal world for a particular set of purposes’. Another definition mentioned inthe same source refers to ‘...checking, manipulating, and analysing data, whichare spatially referenced to the Earth’.Its capacity to analyse and visualise data makes R a good choice for spatialdata analysis. For some spatial analysis projects, using only R may be sufficientfor the job. In many cases, however, R will be used in conjunction with GISsoftware and possibly a GIS data base as well. Chapter 4 will show how spatialdata are imported from and exported to GIS file formats. As is often the casein applied data analysis, the real issue is not whether a given problem can be
Sedang diterjemahkan, harap tunggu..
(I) Adalah jauh lebih mudah untuk memindahkan data melalui paket statistik spasial. Para
kelas baik didukung langsung oleh paket, membaca dan menulis
data dalam kelas ruang yang baru, atau tidak langsung, misalnya dengan menyediakan
konversi data antara kelas sp dan kelas paket dalam sebuah
paket antarmuka. Opsi terakhir ini membutuhkan satu-ke-banyak hubungan antara
paket, yang lebih mudah untuk menyediakan dan memelihara daripada banyak-ke-banyak
link.
(ii) Kelas baru datang dengan satu set teruji metode (fungsi) untuk
merencanakan, percetakan, subsetting, dan meringkas objek spasial, atau menggabungkan
(overlay) jenis data spasial.
(iii) Paket dengan interface untuk sistem informasi geografis (GIS), untuk
membaca dan menulis format file GIS, dan koordinat (re) proyeksi
kode dukungan kelas baru.
(iv) Metode baru termasuk plot Lattice, AC plot, metode petak
yang menggabungkan titik, garis, poligon, dan grid dengan unsur-unsur peta (referensi
grid, bar skala, panah utara), simbol derajat (seperti dalam 52◦N ) di sumbu
label, dll
Bab 2 memperkenalkan kelas dan metode yang disediakan oleh sp, dan membahas
beberapa rincian implementasi. Bab selanjutnya akan menunjukkan tingkat
integrasi kelas sp dan metode dan paket-paket yang digunakan untuk statistik
analisis data spasial.
Gambar 1.1 menunjukkan bagaimana penerimaan kelas sp telah mempengaruhi
lanskap paket kontribusi; interfacing paket lain untuk menangani dan
menganalisa data spasial biasanya sederhana seperti yang kita lihat di Bagian II. Node yang diarsir
dari grafik ketergantungan paket (co) -written dan / atau dikelola oleh
penulis buku ini, dan akan digunakan secara luas dalam bab-bab berikut.
1.3 R dan GIS
1.3.1 Apa GIS?
Penyimpanan dan analisis Data spasial secara tradisional dilakukan di Informasi Geografis
Sistem (GIS). Menurut definisi berbasis toolbox dari Burrough
dan McDonnell (1998, hal. 11), GIS adalah '... satu set alat yang kuat untuk mengumpulkan,
menyimpan, mengambil di akan, mengubah, dan menampilkan data spasial dari
dunia nyata untuk satu set tertentu tujuan '. Definisi lain yang disebutkan dalam
sumber yang sama mengacu pada '... memeriksa, memanipulasi, dan menganalisis data, yang
secara spasial direferensikan ke Bumi.
Kapasitasnya untuk menganalisis dan memvisualisasikan data membuat R pilihan yang baik untuk spasial
analisis data. Untuk beberapa proyek analisis spasial, hanya menggunakan R mungkin cukup
untuk pekerjaan. Dalam banyak kasus, bagaimanapun, R akan digunakan bersama dengan GIS
software dan mungkin data base GIS juga. Bab 4 akan menunjukkan bagaimana spasial
data yang diimpor dari dan diekspor ke format file GIS. Seperti yang sering terjadi
dalam analisis data diterapkan, masalah sebenarnya adalah bukan apakah masalah yang diberikan dapat
kelas baik didukung langsung oleh paket, membaca dan menulis
data dalam kelas ruang yang baru, atau tidak langsung, misalnya dengan menyediakan
konversi data antara kelas sp dan kelas paket dalam sebuah
paket antarmuka. Opsi terakhir ini membutuhkan satu-ke-banyak hubungan antara
paket, yang lebih mudah untuk menyediakan dan memelihara daripada banyak-ke-banyak
link.
(ii) Kelas baru datang dengan satu set teruji metode (fungsi) untuk
merencanakan, percetakan, subsetting, dan meringkas objek spasial, atau menggabungkan
(overlay) jenis data spasial.
(iii) Paket dengan interface untuk sistem informasi geografis (GIS), untuk
membaca dan menulis format file GIS, dan koordinat (re) proyeksi
kode dukungan kelas baru.
(iv) Metode baru termasuk plot Lattice, AC plot, metode petak
yang menggabungkan titik, garis, poligon, dan grid dengan unsur-unsur peta (referensi
grid, bar skala, panah utara), simbol derajat (seperti dalam 52◦N ) di sumbu
label, dll
Bab 2 memperkenalkan kelas dan metode yang disediakan oleh sp, dan membahas
beberapa rincian implementasi. Bab selanjutnya akan menunjukkan tingkat
integrasi kelas sp dan metode dan paket-paket yang digunakan untuk statistik
analisis data spasial.
Gambar 1.1 menunjukkan bagaimana penerimaan kelas sp telah mempengaruhi
lanskap paket kontribusi; interfacing paket lain untuk menangani dan
menganalisa data spasial biasanya sederhana seperti yang kita lihat di Bagian II. Node yang diarsir
dari grafik ketergantungan paket (co) -written dan / atau dikelola oleh
penulis buku ini, dan akan digunakan secara luas dalam bab-bab berikut.
1.3 R dan GIS
1.3.1 Apa GIS?
Penyimpanan dan analisis Data spasial secara tradisional dilakukan di Informasi Geografis
Sistem (GIS). Menurut definisi berbasis toolbox dari Burrough
dan McDonnell (1998, hal. 11), GIS adalah '... satu set alat yang kuat untuk mengumpulkan,
menyimpan, mengambil di akan, mengubah, dan menampilkan data spasial dari
dunia nyata untuk satu set tertentu tujuan '. Definisi lain yang disebutkan dalam
sumber yang sama mengacu pada '... memeriksa, memanipulasi, dan menganalisis data, yang
secara spasial direferensikan ke Bumi.
Kapasitasnya untuk menganalisis dan memvisualisasikan data membuat R pilihan yang baik untuk spasial
analisis data. Untuk beberapa proyek analisis spasial, hanya menggunakan R mungkin cukup
untuk pekerjaan. Dalam banyak kasus, bagaimanapun, R akan digunakan bersama dengan GIS
software dan mungkin data base GIS juga. Bab 4 akan menunjukkan bagaimana spasial
data yang diimpor dari dan diekspor ke format file GIS. Seperti yang sering terjadi
dalam analisis data diterapkan, masalah sebenarnya adalah bukan apakah masalah yang diberikan dapat
Sedang diterjemahkan, harap tunggu..
Bahasa lainnya
Dukungan alat penerjemahan: Afrikans, Albania, Amhara, Arab, Armenia, Azerbaijan, Bahasa Indonesia, Basque, Belanda, Belarussia, Bengali, Bosnia, Bulgaria, Burma, Cebuano, Ceko, Chichewa, China, Cina Tradisional, Denmark, Deteksi bahasa, Esperanto, Estonia, Farsi, Finlandia, Frisia, Gaelig, Gaelik Skotlandia, Galisia, Georgia, Gujarati, Hausa, Hawaii, Hindi, Hmong, Ibrani, Igbo, Inggris, Islan, Italia, Jawa, Jepang, Jerman, Kannada, Katala, Kazak, Khmer, Kinyarwanda, Kirghiz, Klingon, Korea, Korsika, Kreol Haiti, Kroat, Kurdi, Laos, Latin, Latvia, Lituania, Luksemburg, Magyar, Makedonia, Malagasi, Malayalam, Malta, Maori, Marathi, Melayu, Mongol, Nepal, Norsk, Odia (Oriya), Pashto, Polandia, Portugis, Prancis, Punjabi, Rumania, Rusia, Samoa, Serb, Sesotho, Shona, Sindhi, Sinhala, Slovakia, Slovenia, Somali, Spanyol, Sunda, Swahili, Swensk, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Turki, Turkmen, Ukraina, Urdu, Uyghur, Uzbek, Vietnam, Wales, Xhosa, Yiddi, Yoruba, Yunani, Zulu, Bahasa terjemahan.
- Ban se gui hinh anh em gai ban?
- Story of Technical WritingHistory of Wri
- I heve not patience
- Aku lelah dengan semua ini..
- Inflammatory
- many GIS packages do provide a 2.5D inte
- Aku tidak akan jawab itu sekarang
- shame on you!!!
- Malu pada anda
- the appropriate print method, but we can
- In Section B, one of the vulnerability d
- Nanti juga tahu bila waktunya tiba. Aku
- In Section B, one of the vulnerability d
- the appropriate print method, but we can
- In Section B, one of the vulnerability d
- Loved
- nhieu
- > typeof(cars)[1] "list"> names(cars)[1]
- Aku lelah dengan semua ini..
- I waiting bat you not
- Tempat yang indah
- Di saat aku memerlukan bantuan apa perna
- barracks
- Nanti juga tahu bila waktunya tiba. Aku
