20250530
08274900
1.0
FALSE
PEUS.uso_zon_macrozoneamento_municipal
Server=172.31.12.203; Service=sde:sqlserver:172.31.12.203,15010; Database=dbSIG; User=peus; Version=sde.DEFAULT
ArcSDE Connection
002
635914.115900
691514.362600
7566427.794400
7619352.127500
1
0.000
Geographic
GCS_SIRGAS_2000
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.1.0'><WKT>GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4674]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>8.983152841195215e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4674</WKID><LatestWKID>4674</LatestWKID></GeographicCoordinateSystem>
FeatureClassToFeatureClass MACROAREAS\MACROZONEAMENTO_MA1\MACROZONA_MA1 R:\ssplat\dgeo\SIGPLAN.sde\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA MACROZONA_MA1_F # "NAME "NAME" true false false 100 Text 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,NAME,-1,-1;SHAPE_Length "SHAPE_Length" false true true 8 Double 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,SHAPE_Length,-1,-1;SHAPE_Area "SHAPE_Area" false true true 8 Double 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,SHAPE_Area,-1,-1;MACROAREA "MACROAREA" true true false 20 Text 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,MACROAREA,-1,-1;MACROZONA "MACROZONA" true true false 20 Text 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,MACROZONA,-1,-1;DESCRICAO "DESCRICAO" true true false 1000 Text 0 0 ,First,#,R:\ssplat\dgeo\TELMA\CAMARA\CORRECOES.gdb\MACROZONA_MA1,DESCRICAO,-1,-1" #
Project "Conexões do Banco de Dados\NOVO_BANCO_SIGPLAN.sde\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\dbsig.SIGPLAN.MACROZONA_MA1_F" "C:\Users\sepur\Desktop\[MAT] SIGPLAN SIRGAS2000\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONA_MA1_F.shp" PROJCS['SIRGAS_2000_UTM_Zone_23S',GEOGCS['GCS_SIRGAS_2000',DATUM['D_SIRGAS_2000',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] SAD_1969_To_SIRGAS_2000_2 PROJCS['SAD_1969_UTM_Zone_23S',GEOGCS['GCS_South_American_1969',DATUM['D_South_American_1969',SPHEROID['GRS_1967_Truncated',6378160.0,298.25]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] NO_PRESERVE_SHAPE # NO_VERTICAL
CopyFeatures "C:\Users\sepur\Desktop\[MAT] SIGPLAN SIRGAS2000\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONA_MA1_F.shp" "Conexões do Banco de Dados\NOVO_BANCO_SIGPLAN.sde\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\dbsig.SIGPLAN.MACROZONA_MA1_F" # 0 0 0
Merge dbsig.SIGPLAN.MACROZONA_MA1_F;dbsig.SIGPLAN.MACROZONA_MA2_F;dbsig.SIGPLAN.MACROZONA_MA3_F C:\Users\sepur\Desktop\Teste\merge.shp "OBJECTID "OBJECTID" true true false 4 Long 0 10,First,#,dbsig.SIGPLAN.MACROZONA_MA1_F,OBJECTID,-1,-1,dbsig.SIGPLAN.MACROZONA_MA2_F,OBJECTID,-1,-1,dbsig.SIGPLAN.MACROZONA_MA3_F,OBJECTID,-1,-1;NAME "NAME" true true false 100 Text 0 0,First,#,dbsig.SIGPLAN.MACROZONA_MA1_F,NAME,0,100,dbsig.SIGPLAN.MACROZONA_MA2_F,NAME,0,100,dbsig.SIGPLAN.MACROZONA_MA3_F,NAME,0,100;MACROAREA "MACROAREA" true true false 20 Text 0 0,First,#,dbsig.SIGPLAN.MACROZONA_MA1_F,MACROAREA,0,20,dbsig.SIGPLAN.MACROZONA_MA2_F,MACROAREA,0,20,dbsig.SIGPLAN.MACROZONA_MA3_F,MACROAREA,0,20;MACROZONA "MACROZONA" true true false 20 Text 0 0,First,#,dbsig.SIGPLAN.MACROZONA_MA1_F,MACROZONA,0,20,dbsig.SIGPLAN.MACROZONA_MA2_F,MACROZONA,0,50,dbsig.SIGPLAN.MACROZONA_MA3_F,MACROZONA,0,20;DESCRICAO "DESCRICAO" true true false 254 Text 0 0,First,#,dbsig.SIGPLAN.MACROZONA_MA1_F,DESCRICAO,0,254,dbsig.SIGPLAN.MACROZONA_MA2_F,DESCRICAO,0,254,dbsig.SIGPLAN.MACROZONA_MA3_F,DESCRICAO,0,254" NO_SOURCE_INFO
FeatureClassToFeatureClass merge C:\Users\sepur\appdata\Roaming\ESRI\Desktop10.5\ArcCatalog\NOVO_BANCO_SIGPLAN.sde\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA MACROZONAS # "OBJECTID "OBJECTID" true true false 10 Long 0 10,First,#,merge,OBJECTID,-1,-1;NAME "NAME" true true false 100 Text 0 0,First,#,merge,NAME,0,100;MACROAREA "MACROAREA" true true false 20 Text 0 0,First,#,merge,MACROAREA,0,20;MACROZONA "MACROZONA" true true false 20 Text 0 0,First,#,merge,MACROZONA,0,20;DESCRICAO "DESCRICAO" true true false 254 Text 0 0,First,#,merge,DESCRICAO,0,254" #
CopyFeatures "Database Connections\NOVO_BANCO_SIGPLAN.sde\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\dbsig.SIGPLAN.MACROZONAS" "C:\Users\sepur\Desktop\[MAT] BACKUP SIRGAS2000 ERRADO\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS.shp" # 0 0 0
Project "C:\Users\sepur\Desktop\[MAT] BACKUP SIRGAS2000 ERRADO\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS.shp" "C:\Users\sepur\Desktop\[MAT] BACKUP SAD69\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS.shp" PROJCS['SAD_1969_UTM_Zone_23S',GEOGCS['GCS_South_American_1969',DATUM['D_South_American_1969',SPHEROID['GRS_1967_Truncated',6378160.0,298.25]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] SAD_1969_To_SIRGAS_2000_2 PROJCS['SIRGAS_2000_UTM_Zone_23S',GEOGCS['GCS_SIRGAS_2000',DATUM['D_SIRGAS_2000',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] NO_PRESERVE_SHAPE # NO_VERTICAL
Project "C:\Users\sepur\Desktop\[MAT] BACKUP SAD69\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS.shp" "C:\Users\sepur\Desktop\[MAT] BACKUP SIRGAS2000 CERTO\dbsig.SIGPLAN.PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS.shp" PROJCS['SIRGAS_2000_UTM_Zone_23S',GEOGCS['GCS_SIRGAS_2000',DATUM['D_SIRGAS_2000',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] SAD69_SIRGAS2000_GEOCENTRIC PROJCS['SAD_1969_UTM_Zone_23S',GEOGCS['GCS_South_American_1969',DATUM['D_South_American_1969',SPHEROID['GRS_1967_Truncated',6378160.0,298.25]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',10000000.0],PARAMETER['Central_Meridian',-45.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] NO_PRESERVE_SHAPE # NO_VERTICAL
CopyFeatures MACROZONAS C:\Users\leonardo\Documents\ArcGIS\Projects\ArquivoBase_2022\GEODB_COMPLETO_23.08.gdb\PDP_2015_POS_CONFERENCIA_CAMARA\MACROZONAS # # # #
CalculateField "Macrozoneamento Municipal de Juiz de Fora" fonte "Anexo N° 8 do PDP 2018" VB #
CalculateField "Macrozoneamento Municipal de Juiz de Fora" ano "2018" VB #
CalculateField "Macrozoneamento Municipal de Juiz de Fora" legislacao_especifica "Dados da camada “dbsig.SIGPLAN.MACROZONA_MA1_F”, do Anexo Nº 8 do PDP 2018" VB #
CalculateField "Macrozoneamento Municipal de Juiz de Fora" legislacao_especifica "Anexo Nº 8 do PDP 2018" VB #
CalculateField "Macrozoneamento Municipal" area_geometria [shape_Area] VB #
CopyFeatures C:\Users\sepur\Downloads\BANCO\P4_DADOS_PEUs_8.gdb\uso_zon_macrozoneamento_municipal C:\Users\sepur\AppData\Roaming\ESRI\Desktop10.8\ArcCatalog\SISURB_peus.sde\PEUS.produto_4_versao_final\PEUS.uso_zon_macrozoneamento_municipal # # # #
20250729
04154700
20250729
04154700
ISO 19139 Metadata Implementation Specification GML3.2
150000000
5000
ISO19139
PEUS.uso_zon_macrozoneamento_municipal
Feature Class
0
OBJECTID
ID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
MACROAREA
Macroárea
String
20
0
0
nro
Nro
Integer
4
10
0
nome
Nome
String
100
0
0
DESCRICAO
Descrição
String
254
0
0
sigla
Sigla
String
20
0
0
observacao
Observação
String
255
0
0
Shape
Geometria
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
fonte
Fonte
String
50
0
0
ano
Ano
String
50
0
0
area_geometria
Área.Geometria(m²)
Double
8
38
8
legislacao_especifica
Legislação.Específica
String
150
0
0
shape.STArea()
shape.STArea()
Double
0
0
0
shape.STLength()
shape.STLength()
Double
0
0
0
PDP 2018, Território, Macrozoneamento
Classe de feição (polígono) que delimita e identifica a Macroárea 3 (Macroárea de Interesse Ambiental e de Preservação dos Mananciais) alocada dentro do perímetro municipal de Juiz de Fora, objeto do Anexo N° 7 do PDP 2018.. As macroáreas são "áreas que guardam aspectos gerais - ambientais, geográficos e antrópicos - que as identificam e as distinguem das demais, orientando ao nível do território, os objetivos específicos de desenvolvimento urbano e a aplicação dos instrumentos urbanísticos e ambientais" (LC 082/2018).
Delimitar e identificar a Macroárea 3 (Macroárea de Interesse Ambiental e de Preservação dos Mananciais) alocada dentro do perímetro municipal de Juiz de Fora, objeto do Anexo N° 7 do PDP 2018.
ArcGIS Metadata
1.0
SDE Feature Class
unknown
0.000
uso_zon_macrozoneamento_municipal
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Camada de dados geográficos </SPAN><SPAN>que delimita, identifica e caracteriza a Macrozona 1 (dentro da Macroárea 1) alocada dentro do perímetro municipal de Juiz de Fora, objeto do Anexo N° 8 do PDP 2018. A Macrozona 1 é dividida em: I - Macrozona do Eixo do Paraibuna - MZP; II - Macrozona de Consolidação e Qualificação Urbana - MZQ; III - Macrozona de Requalificação e Estruturação Urbana - MZR; IV - Macrozona de Expansão do Desenvolvimento Urbano - MZE; V - Macrozona de Requalificação e Contenção Urbana - MZC.</SPAN></P></DIV></DIV></DIV>
Delimitar, identificar e caracterizar a Macrozonas dentro das Macroáreas alocadas dentro do perímetro municipal de Juiz de Fora, objeto do Anexo N° 8 do PDP 2018.
Dados encaminhado pela Prefeitura Municipal de Juiz de Fora em 2023.
dbsig.SIGPLAN.MACROZONA_MA1_F
[Fonte Anexo Nº 8 do PDP 2018]
PDP 2018, Território, Macrozoneamento
PDP 2018 | Território | Macrozoneamento | Macroáreas
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.1.0.3035
1
-43.687693
-43.144899
-21.518328
-22.001447
Classe de feição criada a partir de indicações locacionais estabelecidas na legislação municipal (LC N° 082/2018).
0
EPSG
7.9.4
6.11(9.2.0)
Simple
FALSE
0
TRUE
FALSE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