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Introduction: - a. Purpose According to U.S. Census Bureau the population of stark county was 367,585 in 1990, and in 2000 it was increase to 378,098. Thus County is experiencing a growth that presents challenges related to utility infrastructure planning. Many of these challenges are related to water supply, water delivery, wastewater collection, changing source water qualities, and maintenance, travel behavior, traffic congestion and air quality. A land use database will enable the accurate monitoring of shifts in land use and help in the debates on land availability, urban expansion and protection of the countryside. The principal purpose of this geodatabase is to store and document data for departments of planning. b. Scope The Geodatabase focuses on Stark County which is located in the Ohio State. The goal of this project is to present Geodatabase for Stark County Land use. This Land use Data Model is designed as an essential data model that provides a context within a GIS that can quickly and easily accept the input that from a wide range of users need in order to accomplish their Land use goals. It allows us to examine the current Land use framework and issues within the framework. Considering the rapid and fast growth of the county, the project will enable us to solve issues within its structure to provide better solutions such as reducing congestion and pollution, expanded transportation infrastructure, accessibility to emergency service etc. 2
c. Background The Stark County is located in Northeast quadrant of Ohio; it is situated approximately 20 miles south of Akron, 53 miles south of Cleveland, 125 miles northwest of Pittsburgh and 386 miles east of Chicago. Northeast Ohio is also important due to its strategic location at the crossroads of the Midwestern and Eastern U.S. markets. As such, Northeast Ohio provides first-class infrastructure and Logistical support with a wellestablished transportation network connecting several Modes of transportation: including rail, air, water and highways. According to the U.S. Census Bureau, the county has a total area of 581 square miles (1,505 km²).576 square miles (1,492 km²) of it is land and 5 square miles (12 km²) of it (0.82%) is water, with the population was 378,098. 3
Conceptual Data Model A Conceptual Data Model covers one Subject Area from the Enterprise Data Model. The five logical groups in ArcGIS are Stark_Block_Demographics, Stark_Land use, Stark_Hydrology, Stark_Transportation and Stark_Zoning. Concepts: 1. Stark_Block_Demographics: This dataset have the four feature classes showing demographics, Census Tracts, block group and the Stark_Block_Demographics Block_groups Blocks_Demogroup Census_Tracts Parcel Parcels of the Stark County. This dataset contain the information of census 2000 of demographics of the County. 2. Stark_Land use: The logical group contains all the information for the particular class in Land use dataset. Thirteen feature classes are added to this dataset. As to know the particular location of schools, parks, libraries, cemeteries etc and also the use of land in county. 3. Stark_Hydrology: This dataset contain all the water bodies with there cover area in county and location. In the hydrology section it all depends the Stark_Hydrology Lake Well_Fields WaterShed Water_Storage Commerical_WaterTreatment 4 Rivers_Streams
type of research that it is intended to do. Some of the most important attributes are the description of a river or a stream. Other types of attributes important for a planning point of view are the extent of the watersheds and lakes. The depth of water bodies is also very important for developing an area. 4. Stark_Transportation: Transportation dataset has the facilities which are available for transportations in stark county. This dataset include three feature classes of Air fields, Main Roads Stark_Transportation Airfields Main_Roads Rail_Roads and Rail Roads to know the accessibility for the desire destination and the mode of transportation. For e.g.:- Getting people to and from their homes, businesses and community facilities is quite important. Without proper planning for current traffic and parking, as well as future growth, an area can be negatively impacted and result in no growth or shrinkage in population. 5. Stark_Zoning: Stark_Zoning Zoning includes the Land use. A county, in developing a plan for growth, would first need to determine the accepted uses for the land, in what proportions for each use and Commercial Industrial Residential rules for implementation of the plan. Depending on the area, this would include using land for residential, commercial and industrial. 5
6. Stark_AerialPhoto: Aerial photo includes the Raster image of Stark County. This can be use for Land use location. As the database is about the Land use of Stark County so it will be helpful in planning. Attributes and Relationship 1. Stark_Block_Demographics: In this section we have a diverse data about the specific demographic of Stark County. This feature class has a relationship with a table inside the geodatabase.this is more specific about the Demographics; the relationship is one to one between the Census_tract and Tract_demographics. 2. Stark_Landuse: Land use dataset has thirteen feature classes. In this section one to one relationship is shown between the Cemeteries and Parcel number feature class. 3. Stark_Hydrology: In the hydrology section it all depends the type of research that it is intended to do. Some of the most important attributes are the description of a river or a stream. Other types of attributes important for a planning point of view are the extent of the watersheds, waterstorage and lakes. In the River_stream feature class subtypes are created for easy use. 6
4. Stark_Transportation: In the transportation section we have the most attributes possible because it is very important to develop around or near the roads. This section contains the locations by county of almost all of its feature classes. The Main_roads section contains a subtype of the type of road in terms of state, county, Interstate etc. This feature class also contains two range domains one of them is number of lanes and the other one is speed limit. These values are very important because it will give us more detailed information about any street in Stark County. 5. Stark_Zoning: In this section there are three feature classes, this section include topology rule. Logical Data model Logical data models, properly designed, is based on the structures identified in the conceptual data model. Logical models depict the true relationships of attributes as they are grouped into entities, relating attributes to attributes and entities to entities. Logical models are not concerned with implementation, storage mechanisms, and redundancy of data. Logical models are usually normalized. Normalized means that every attribute is independent, that is, not dependent on any other attributes. In this section the true relationship is shown between the Tract_demographics and census Tracts. The other relationship is done between the Parcel_number and Cemeteries. 7
Data source The most important aspect of this data knows where it came from and who edited or processed this data. All of these factors can be found in the metadata. The Source of this all data is from the SCRPC (Stark County Regional Planning Commission).The most of data came in the same Coordinates. Mostly all data was in shape file format and then converted to a geodatabase. Documentation and Schema The Schema of this file geodatabase was design to serve a planning consultation. Some of the data was process in order to set priority hierarchy to more important features when starting a plan. The dataset Stark_Block_Demographics contains the general information about the Stark County. Another dataset Stark_Hydrology contains the information of the water bodies in the Study area. Third is Stark_Transportaion that have types of transportation available in County. Fourth is Stark_Zoning which includes the three main zoning sections, as it is very help full to know the land use. Fifth is stark_landuse in this dataset there are thirteen feature classes to show the location and use of land for various utilizes in the county. There is one Stark_Aerialphoto that is the raster dataset to show the aerial photo of the County. 8
Arc Diagrammer 9
Physical Data Model The logical model was implemented in the physical data model by following the same schema diagram. The physical model was implemented using the most important reference features from a Planning point of view. This file geodatabase was divided into five feature dataset: Stark_Block_Demographics, Stark_Hydrology, Stark_Transportation, Stark_Landuse, and Stark_Zoning beside this one Raster dataset. The logical model was developed by taking under consideration the major steps in study area. The Planners have to look for the data that it is stored in this geodatabase in order to have knowledge of what they are looking for in regards of the attributes. In this case there was only a production of a few subtypes because of the time frame but in further applications other types of subtypes and domains can be created for more feature classes. The creation of subtypes and domains are very important when you have a feature class with a lot of attributes. In this database the feature class River_streams in which according to the feature subtypes is created. For creating the Geodatabase the File Geodatabase is used as the database created is for the Land use of the Stark County. So, it is for the Planning purpose, it means that multiple people are going to use this database and edit it for there particular interest. After taking the planner s major concerns towards the storage of the data the best selection for the geodatabase is the file geodatabase. 10
Feature Dataset by Feature Classes: Stark_Block_Demographics: The first Feature dataset is the Block_Demographics and it contains four feature classes. This section also contains one relationship class of the Census tract 2000, with the table at the last of the geodatabase called Tract_demographics. The section has four feature classes where all four are polygon feature. Stark_LandUse: In this dataset there are thirteen features Classes out which twelve are point and one is polygon that is Parks. 11
Stark_Hydrology: In this dataset there are six feature classes. In which two classes are polygons and three points and two are line. Also include the subtypes in the Feature class River_Streams, subtypes are created on the basis of the features. The Subtypes were given the particular number 1 = Creek, 2 = Pond and 3 = River. 12
Stark_Transportation: This dataset include three classes out of which two are line and one is point. In this dataset the class name Main Roads the subtypes have been created on the basis of the Road types. In which 1 = County Roads, 2 = Interstate Roads and 3 = State Roads. In this feature class Domains are also created, one of them was the Speed Limit and the other one was number of lanes. This was created so that it will be easier to classify in more specific manner the streets in Stark County. 13
Stark_Zoning: In this section there are three features set and all three are polygon feature set. In this section topology rule is created between the Commercial and Industries classes with must not overlap rule. In Catalog tree it is name as Stark_Zoning_Topology. Validate Topology: 14
Summary of the File Geodatabase Build Process a. Loading data: The loading data process was done in different approach and different ways. In this section we will describe some of this process and the value of them to our data. Most of the data was in shape file format with different projections such as StatePlane_Ohio_North_FIPS_3401_Feet.I didn t change the coordinate system of any shape file as for mostly all the shape file it was the same as mention above. After creating the file geodatabase the next step was to select in the geodatabase to import and selecting the multiple feature class. For this I used a tool Import to multiple and then feature class was created to make the organized geodatabase.the loading time for each feature classes varies as the size of the file. The time for biggest time shape file took me around 8 minutes 12 seconds and the smallest shape file just took me 18 seconds. The biggest data which I had was the Aerial photo of Stark County which I loaded using Raster Catalog as Unmanaged Raster type then too it took me around 49 minutes and 3 seconds, and I did not tried the managed Raster type because it would have taken me more than 10 hours. 15
G e o d a t a b a s e B u i l d P r o c e s s Feature Dataset Feature Class Subtype Domains Topology Relationship Stark_Block_Demogrpahics Block_Group Block_Demogroup Census_Tract Parcels Census Tract to Block Demographics Stark_Landuse Stark_Hydrology Stark_Transportation Stark_Zoning Stark_AerialPhoto Cemeteries Church Firestation Govt.Facilities Historical Landmark Hospital Libraries Mines Monuments Parks Police station Post office School Commerical_wt._TreatmentPlant Lake River_Stream Water_Storage Well_Fields Air_Fields Main Roads Rail_roads Commercial Industries Residential AerialPhoto Creek Pond River State County Interstate Lane SpeeLimit Commercial Industries Must not Overlap Parcel to Cemeteries b. Documentation of loading time: Name of Layer Original File File to FGDB Loading Time Entire layers* Shape file Feature Classes 58 minutes Unmanaged images Tiff Raster Catalog 49min - 3sec Managed images Tiff Raster Catalog Not done Rivers_streams(biggest) Shape file Feature Class 8min 12 sec Hospitals Shape file Feature Class 18 seconds 16
C. Model for loading data This model is only a representative of the actual database created but it can be used for any file-geodatabase that needs to be developed. Depending on the quantity of feature classes and raster images imported into the database the time for the development of the geodatabase will vary. 17
Deployment and Assessing Usability In this particular case we are going to use our file geodatabase in order to proof that it is faster. It is no secret that regional land-use plans are not regarded as likely to have any real impact on developer decision-making activities in the United States. The geodatabase can be used for the creating a land use map of whole Stark County or for each township of County. Land use involves all the factors that are Natural subsystem and the Human subsystem. Natural like River, Streams, Lake, Topography and Human includes the infrastructure. As increase in population require more residential, commercial, transportation facilities etc. For planning all this land use is very important. For e.g.: Transportation is part of Land use, so data can be used to show the network of roads, railways, bus stops etc. 1. SARTA: Bus Routes in Stark County. 18
2. Zoning Map of Different Township in Stark County. Nimishillen Township Zoning Map of East Canton Village 19
References: The United States Census Bureau 2000. Web Site: http://www.census.gov Stark County Regional Planning. Web Site: http://www.co.stark.oh.us/internet/home.displaypage?v_page=rpc http://www.esri.com/ http://realestate.about.com/od/ownershipandrights/a/land_and_zoning.htm http://www.vinfiniti.com/2.html http://www3.uakron.edu/geography/students/ktr5/pages/gisdatabasedesign.html http://www3.uakron.edu/geography/students/jam174/geodatabase.htm http://www3.uakron.edu/geography/students/mc68/index.htm 20