Developing a Geological Management Information System: National Important Mining Zone Database

Journul of China University of' Geosciences , Vol. 17, No. 1 , p . 79 - 83 , Murch 2006 Printed in Chinu

I S S N 1002- 0705

Developing a Geological Management Information System : National Important Mining Zone Database

Zuo Renguang" (g{IJ'-) Wang Xinqing (t'$$R) Xia Qinglin (BE%) State K e y Laboratory o f Geological Processes and Mineral Resources, China University o f Geosciences , Wuhan 430074, China ; Faculty o f

Earth Resources, China University o f Geosciences , Wuhan 430074, China

ABSTRACT:Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database (NIMZDB) to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues: @ data accuracy: integrity, logic consistency, attribute, spatial and time accuracy; @ management of both attribute and spatial data in the same system; @ transforming data between MapGIS and ArcGIS; @I data sharing and security; @ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using ArcSDE, we based data sharing on a client/ server system, and attribute and spatial data are also managed in the same system. KEY WORDS: geological management information system, checking data, ArcSDE, transforming data format, data sharing, data security.

INTRODUCTION Geographic information systems (GIS) is a new

technology of storing and processing spatial information, which can combine graphics with many types of database. It can also exhibit accurate and real space information with charts and texts according to actual need, and can integrate geographic locations and cor- related data attributes as an organic whole. Geoscien- tists have shown GIS to be a very useful tool in the analysis of geoscience problems (Zhao et al. , 2004; Singer, 1993) and with the help of CIS and management information system (MIS), we can manage the geo-data efficiently. Therefore, in recent years,

This paper IS financially supported by the National Important Mining Zone Database (No. 200210000004) and Prediction and Assessment of Mineral Resources and Social Service (No. 1212010331402). * Corresponding author: zrguang1981@126. com

Manuscript received August 20, 2005. Manuscript accepted November 18, 2005.

many spatial databases have been developed to manage geological data in every field of geosciences. Al- though many studies have already been performed on the geological management information system (GMIS), especially on both the spatial data model and metadata (Li D P, 2005 ; Fang and Zhong, 2004 ; Li Y D, 2004; Zen and Gong, 2004; Wu et al. , 1996), more studies are needed to determine the character and future development of GMIS.

Geo-data is used frequently and stored over a long period. It is a valuable resource for many social economic activities, for sustainable economic development, and for resource and energy source management by municipal governments and enterprises. It is also important to share geo-data, as the data are costly and are used in many different research pro- jects. Thus, geo-data is an important part of the state geological information system. So it was very significant to research and develop the GMIS.

We have found GMIS to be different from ordi-

80

1 1 Exploration extent map

: 2 Geological and mineral map

Zuo Renguang, Wang Xinqing and Xia Qingli

I , 9 , 0 ,

: ; Data checking reports I I ’

nary management information systems ( M I S ) , because of the complexity and diversity of geoscience data (Zuo and Wang, 2005; Zuo, et al. , 2005; W u et al. , 1996) . Firstly, GMIS manages both non- spatial data and spatial data, while MIS primarily manages non-spatial data. Secondly, GMIS usually administers multi-subject data while MIS manages single subject data. Finally, developing GMIS is based on both GIS and commercial databases such as SQL Sever, Oracle, Sybase , whereas developing MIS is only based on commercial databases. Re- searchers agree that GMIS will play an important role in managing geo-data ( W u et al. , 2005; Wu et al. , 1996), but a few problems, such as uniform management of both spatial data and non-spatial data, uniform querying data, require solutions when developing GMIS. More studies are needed to determine how to develop GMIS and share data.

METHOD The National Important Mining Zone Database

(NIMZDB) is used as an example to discuss the main processes for developing GMIS. NIMZDB is a GMIS that manages a geography database, geological data-

base, ore deposit database, magnetism database, gravity database, geochemical database, and remote sensing database. T h e data are collected from 14 important mining zones in China.

Data Model The data model is a key element in developing a

database. A data model was established by Ma et al. (2005) , based on the standards of the “14 National Important Mining Zone Database”, which is issued by the China Geological Survey (CGS). As shown in Fig. 1 , three kinds of datasets were collected: basic data, production maps and documents. T h e content of the basic data was decomposed into seven modes: geography, geology, mineral deposit, aeromagnetics, gravity, geochemistry and remote sensing. Pro- duction maps of each mineral zone were decomposed into five modes: exploration boundary maps, geological and mineral maps, geological structure maps, mineralization rules and prognosis maps and regional exploration targets suggestion maps. T h e documents collected contain summaries of primary research ( M a et al. , 2005).

I Database I

€3 Basic data

I Seven subjects of basic data 1 Geography 1 2Geology I

17 Remote sensing1

_ _ _ _ _ _ _ _ _ _ _ _ _ _ - _ - _ _ / Borderline / j /Water system/ :

Reference ; One layer contains data i from 14 zones with the i same subject

Production map

I

Fourteen mineral I zones I

-”--H Mineralzone 1 I Mineral zone 2 I( i

Mineral zone 14 I- 1 I

Documents m Fourteen mineral

zones I ,

Mineral zone 1

Mineral zone 2

Mineral zone 14 I I-

Figure 1. Logic model for three data partitions (Ma et al. , 2005).

Checking Data hing and developing a model for checking geo-data GMIS is put forward and developed based on could potentially reduce the administrative time, ma-

MIS. Firstly, owing to the character of the geo-data, terial resources and finance required to maintain the data checking is a compulsory requirement to ensure database, and it can assure the veracity of the data too. that the data are accurate and up to date. Researc- Geo-data is characteristically multi-sourced,

Developing a Geological Management Information System: National Important Mining Zone Database 81

multi-type, multi-dimensional, multi-factor and Primary checking data multi-quality ( W u et al. , 1996), and each subject of geo-data is different in the integrity and format of its data, its spatial scale and projection. In the example of NIMZDB, there are two methods for checking data , one is “Primary checking data”, and the other is “Checking data”.

Primary checking data (PCD) is required to col- lect and investigate what each mining zone work- group submits, including the content of each accom- plished database: geo-data source and format, spatial database scales, projection parameters, and database capacity. And then, we should fill in Table 1.

Table 1 Basic data checking worksheet

Mining zone No. Mining zone name

Data base I Format 1 Scale 1 Projection 1 Source 1 Capacity I Introduction

Geography database

Geological database 1

Ore deposits database I I I I I I Magnetism database

Gravity database

Geochemical database

Remote sensing database

Checking data Checking data (CD) , which is based on the PCD

method, is processed according to certain standards or criteria, such as “ T h e Guidance of National Min- ing Zone Spatial Database” (GNMZSD). CD includes three steps.

The first step T h e first step is the main task, which is to inspect all files and their integrity. Checking the basic database files and productive database files is a necessity.

The second step T h e second step is to check the accuracy of spatial projection parameters. For example, non-projection geography longitude and latitude coordinates in MapGIS files must be multiplied by 400. Checking non-spatial data configuration is another task. According to GNMZSD, regarding each mining zone as a whole, non-spatial data configura- tions must be examined, and the nature of errors and where they occur must be noted.

The third step Checking each spatial element and its feature identity number is required if each feature identity number is unique and standard. On one hand, we should check whether the data are integral and precise to meet what the standards demand. On the other hand, we should also check whether the record is integral and precise (Fig. 2) .

m Data collection

Checking data

r

The report of checking data

Figure 2. The flow of checking data.

UNIFORM MANAGEMENT OF BOTH NON- SPATIAL AND SPATIAL DATA Spatial Data Engine

The spatial data engine ( S D E ) can uniformly manage both spatial data and non-spatial data in a Relation Database Management System ( RDBMS) , which can make full use of powerful functions to run the spatial data associated with non-spatial data through SDE. ArcSDE and Oracle Spatial are well known engines. ArcSDE plays an important role as an application gateway between DBMS and GIS, allo- wing the management of spatial and non-spatial data. ArcSDE exists outside large-scale commercial DBMS, which is especially applicable for large-scale users

82 Zuo Renguang, Wang Xinqing and Xia Qingli

managing huge datasets. As such, it is widely ap- plied in geology.

ArcSDE places all geography elements in one layer, which consists of a business table ( A ) , feature table ( F ) , spatial index table ( S ) and point table

(P) . “A” stores the layer feature. Each record of A

denotes a spatial element and has a unique feature identity (FID) , which is associated with each record of F.

rangement. It corresponds to A.

ment boundary information, and the element FID.

associated with A through FID.

“F” stores geometry elements in a binary ar-

“S” stores the serial number of the grid, the ele-

“P” stores geometry element coordinates, and is

Data Sharing Based on ClienVServer The SDE is located between RDBMS and client,

and is located on the server side with RDBMS. The SDE has an application programming interface (API) on client and server sides. The API in the client end responds to client demand and translates i t into standard SQL. The server side APT exchanges information with RDBMS. Finally, SDE sends the result to the client (Fig. 3 ) and the user can see and manipulate the result in the client end (Qiu, 2005).

I ClientAPI 1 I 1

Client t TCP/IP Server , , Network

SDE Server

e3 Data Forms

ure 3. The US structure for SDE.

Uniformly Querying Data Geo-data contains a lot of spatial and non-spatial

data. SDE links spatial and non-spatial data as a whole in a DBMS, breaking through the ordinary pattern of respectively stored data. This realizes uniformly querying both spatial and non-spatial data.

Transforming Data Format Generally speaking, transforming spatial data

format contains three aspects: spatially located infor-

mation, spatial relationships, and non-spatial data. When transforming the GIS data format into another format, some or all information can be lost. Spatially located and non-spatial data are often wholly trans- formed, but topological information is usually lost or not integrated. This phenomenon was observed when transforming MapGIS files into ArcGIS files because the MapGIS files recognize topology while the Arc- GIS shape file does not. As geo-data often uses these two formats in NIMZDBS, this problem is often con- fronted, and a solution is very important in GMIS. In practice, the lost information is stored in a binary field when transforming MapGIS to ArcGIS. This information can then be reverted to topological information when transferring from ArcGIS to MapGIS.

Data Security Geo-data security is particularly important. In

order to prevent information from being stolen, we should guarantee that some data can be visited and revised by all visitors, and some data can only be visited with special authority or identification. To assure geo-data security, particular emphasis is placed on researching data security when users visit and re- vise data, and on the security of data transmission. Security is considered using the National Important Mining Zone Database as example, where an SQL Server performs on the back side.

SQL Server Security The security of the SQL Server is mainly embod-

ied in log-in identity authentication-SQL Server and Windows N T from identification to authentication, setting database user’s account number, role and authority authentication. During log-in, users supply their user-IDs and passwords. The system checks the existence of the password file. If it exists, the DBMS authorizes the user to enter the system and distrib- utes a role to visit the data according to the password and IP.

Transmission Process Security In the process of data transmission, in order to

prevent the data from being stolen or lost by acci- dent, data encryption and locking technologies should be adopted. This would ensure that stolen data could not be read.

Exporting Data A GMIS should have powerful data exporting

Developing a Geological Management Information System: National Important Mining Zone Database 83

functions. Based on ArcInfo 8. 3 , we have designed and realized data cutting and exporting to personal database, enterprise database, dBase files and Map- GIS files in NIMZDB.

RESULTS GMIS is based on MIS, which can import,

store, query, manipulate, export, update, and maintain geo-data well. Furthermore, GMIS, being characterized as spatial analysis, has the following characteristics.

Bulk Storage Capacity Requirement There are two reasons why geo-data require a

high storage capacity. Firstly, each subject database is very large due to multi-source and multi-type characteristics. Secondly, geo-data is multi-subject , and many subjects are associated with each other. For example, the ore deposit database is usually related to the geochemical database, geophysical database, and remote sensing database, and a special field database can contain several single subject databases, such as the NIMZDB, which is made up of seven subject databases and five productive databases.

Data Model, Data Stored Model and Method Diversity Data models and data stored models are diverse

in GMIS because geo-data is characteristically of multi-type and multi-dimension, and because each GIS has its own data format. Also, when manipula- ting and using geo-data, many methods are used, owing to the multi-subject character of GMIS.

Uniform Management of Both Non-spatial and Spatial Data

Geo-data contains many non-spatial data and vast spatial data, both of which are associated with each other, and the latter plays an important role in mineral resource prediction and assessment. So GMIS should be uniformly managed and manipulated between the non-spatial data and spatial data.

Having Characteristic of Special Subject A geo-data database contains many subjects,

such as geology, aerography , biology and genmatics, while we research on and develope a GMIS for a certain special field, such as ore deposits, which is in- volved in ore deposits database, gravity database, and geochemical database. When developing GMIS for ore deposits, we must research those data charac-

ter to choose the commercial database and GIS plat- form. In our research, we chose SQL Server, Map- GIS and ArcGIS to develop NIMZDB using Visual Basic (copyright 6 .0 ) .

Data Sharing and Security Geo-data can be used for a long time, and can be

shared by multi-users at the same time in a system. In order to make full use of the geo-data, data sharing is necessary. When sharing data in the internet or intranet , another arising problem is the security of the data, to which attention should be paid.

CONCLUSION In developing NIMZDB we have found GMIS to

be apparently different from the MIS, not only in the character of the stored data, but also in the method of software development. Geo-data has multi-source, multi-mass, multi-type, multi-element , multi-dimension, and multi-subject characteristics. The geo-data consists of non-spatial and spatial data. The GMIS is characterized by a need for high storage capacity, data model, data stored model and method diversity, and uniform management of both non-spatial and spatial data. Data sharing and security are also very important to GMIS. To develop GMIS, more attention should be paid to the following issues. 0 Assuring the accuracy of geo-data, including data integrity, logic consistency among spatial data, accuracy of both non-spatial data and time, and the accuracy of spatial element located information. 0 Uniformly managing both non-spatial and spatial data in the same system using a spatial data engine. @ Trans- forming the data formats between MapGIS and Arc- GIs. @ Sharing data and data security. In the Na- tional Important Mining Zone Database, we success- ful ly solved the above problems and based data sharing on a client/ server system, as well as uniformly managing both non-spatial and spatial data in the same system through ArcSDE.

ACKNOWLEDGMENT The research is supported by the National Im-

portant Mining Zone Database ( No. 200210000004) and Prediction and Assessment of Mineral Resources and Social Service (No. 1212010331402). The au- thors would like to thank associate Professor Mei Hong- bo for providing a new idea on the data security.

(Continued on page 94)

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