development of surface cost estimation model for ...

Mining optimization, surface cost, valuation, GIS, acquisitions

Michal W. DUDEK*

DEVELOPMENT OF SURFACE COST ESTIMATION MODEL FOR STRIP MINE USING GIS TOOLS

In open pit mining there is a diversity of mine types based upon the orebody kind. Each of them may vary in size from small local mines to large opencast for instance lignite mines. Land cost in open pit mining in developed countries became topic worth discussion because of growth in density of urban area and high real property cost. This article focuses on differences between vector and rectangular surface cost model and creating spatial database to have further ability to determine for instance the cost of pit or outer dump of waste material location of open pit mine due to land cost, acquisitions and compensations. The created model may enhance abilities of opencast mine design. The main idea presented in this research is that cost analysis in mining projects related to surface may be enhanced using new vector based surface cost model.

1. INTRODUCTION Over the past 35 years the determination of optimum open-pits has been one of the most active areas of research in the mining industry and many algorithms have been published. The most common optimizing criterion in these algorithms is maximization of the overall profit within the designed pit limits subject to mining (access) constraints. Almost all algorithms use a block model of the orebody, and sufficient surrounding waste to allow access to the deepest ore blocks. Among these algorithms Lerchs– Grossmann algorithm, based on graph theory, is the only method that guarantees to point out the true optimum pit (Khalokakaie et al., 2000). The process of open pit long term scheduling consist of finding the final pit limits which maximizes the profit based on Lerchs–Grossmann’s algorithm. The ultimate pit limit design is usually followed by a life-of-mine (LOM) schedule. Aim of this schedule is to maximize the net present value of the operation within the predetermined LG __________ * Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland. e-mail: [email protected]

ultimate pit limits. Currently many advanced models have been created to fit reality. Those models include artificial intelligence, stochastic modeling, implementing time factor in optimisation using discounted economic block values, creating optimal sequence of mine layouts expansions by simulating mining cuts instead of examining individual blocks (Askari-Nasab, Awuah-Offei, 2009). In Lerchs–Grossmann algorithm, one of the most important design factors in opencast mining is the ultimate pit limit design constrained by geological, mining, processing, and technical limitations. The algorithm does not precise the complex geometry of land ownership. For this research spatial database was created of testing area to determine the impact of land parcels geometry and its estimated value on total surface cost. Real estate, surface cost as a constraint is a subject to mining design software. In popular mining optimisation software, pit limits may be defined by restricting ultimate pit to an area within block models in one of two ways. By digitizing or importing an outline outside which mining is not allowed or outlines inside which mining is not allowed. As an option user can choose additional areas that can be removed at a cost only in their entirety or not at all. These costs are treated by this software as capital expenditures. In this case the outlines must be determined by user but to cover different scenarios of possible real estate buyout that generate surface cost this outline should be rather large. Interesting would be treating the area for possible buyout as kind of nested vector outlines as it corresponds to real estate boundaries. Such created model will give more precise information about surface cost. In many mines, for example in Polish lignite opencast mine ‘Konin’ there is department that is planning on a current basis acquisition of certain lands due to direction of pit advancing.

2. RESEARCH AREA Research area Maniow Maniow Maly is a cadastral district of Wroclaw County in Poland and covers approximately 5,3 km2. Selection of the area is a result of data availability and is an attempt to verify the idea of land parcels complex geometry impact on total land cost.

3. DATA COLLECTION AND METHODS OF ANALYSIS The proposed methodology of data collection and spatial database development has been divided into the following stages: creating vector map of Maniow Maly research area, collecting transaction prices from County Cadastral Department,

calculating average prices for different land uses in research area, mass valuation, spatial database development and GIS based software analysis. Spatial data was collected from website geoportal.gov.pl by querying following phrase into web browser that returned a XML file. This file converted into GML (Geography Markup Language) contains geometry and features requested in query. sdi.geoportal.gov.pl/wfs_dzkat/wfservice.aspx?service=wfs&request=getfeature&bbox=50.924008,16. 5823,50.976852,16.69116&version=1.1.0&typename=Dzialki&srsname=EPSG:4326

Land parcel prices are official data from the Wroclaw County Cadastral Department based on notarial acts and appraisal reports in 2003 – 2011. Another source of information is the real estate turnover in 2010 published by Central Statistical Office of Poland in 2011 and Polish land value report from Polish Federation of Valuers' Associations. Other sources were spatial Internet services from both National and County level (geoportal.gov.pl and wrosip.pl) from which spatial data of land parcels and buildings were received. To have the ability to create surface land cost layer it is important to estimate the value of all real properties. In order to proceed with valuation, simplified model of mass valuation as for taxation purposes was used. In this case mass valuation is multiproperty based, unified valuation prepared with valuation techniques use on the exact date. Mass valuation procedure leads to preparation of plats and tables with parameters that will be used to correct value of each property by multiplying average value per square meter by this set of characteristic (International Valuation Standards, 2005). Polish Mass Appraisal and Taxation Ordinance (2005) states that the general proceedings with mass taxation/valuation are similar to International Valuations Standards (ordinance created but not in use). To build mass valuation model a set of representative real properties should be taken to select and calculate characteristic parameters which later will correct other properties in range. Plat maps and tables are also used. For taxation purposes 2 main land types are used, agricultural lands, forests and the rest (considered as urban area). To proceed with real property valuation, to have ability to compare gathered information from County Cadastral Department, in for instance, comparable sales approach these properties (prices) must be updated to the valuation date (the same time frame. For this research it is December 2012). For this purpose linear regression model with one variable was used (Sawiłow, 2011). . where: - updated unit price of ith estate, - unit transaction price of ith estate,

(1)

- date of transaction of ith estate, - valuation date, - function estimated parameter in time, - function estimated parameter in valuation date. Extreme transaction prices were rejected using 2 standard deviations. After rejecting extreme transactions, average price per square meter was calculated from updated unit prices from the 2003–2011 period. Transaction data set is based upon agricultural lands (165 transaction prices) and residential lands (34 transaction prices). As mentioned before land cover area was simplified by splitting into two main land covers, urban and rural areas. Calculated average price per square meter for agricultural lands is 11,56 zł/m2 whereas for residential lands it is 92,36 zł/m2. Table 1 include official transaction prices per square meter from the real estate turnover in 2010 published by Central Statistical Office of Poland in 2011. Compared to the own results from Wroclaw County area (part of Lower Silesia) it may suggest that the calculations were correct or at least not incorrect. Tab. 1. Average measures for purchase/sale transactions of residential and agricultural land by Voivodships (Real Estate Turnover, 2011) Specification: Average area sold in Average value of single Average transaction price a – total, single transaction in m2 transaction in thous. zl in zl/m2 b – urban areas, residential agricultural residential agricultural residential agricultural c – rural areas a 1870,1 16268,9 93,0 63,7 49,74 3,92 Poland b 1564,9 4136,3 109,4 100,6 69,91 24,33 c 2054,3 18178,4 83,1 57,9 40,45 3,19 a 3043,2 17667,2 241,8 84,5 79,44 4,78 Lower Silesia b 2920,1 9717,4 274,1 118,6 93,87 12,20 c 3488,2 19181,4 124,8 78,0 35,77 4,06

GIS as a powerful tool can also be applied in real estate so that it enables the user to capture, store, analyze, and visualize real property information. GIS also became an analytic tool the assessment community used to manage large quantities of data. Mass appraisal, valuation techniques can be found in the private sector valuations. The use of CAMA (computer assisted mass appraisal, the assessment community’s term for mass or multiple property appraisal) technology is beginning to permeate all sectors of the valuation industry, from the agricultural sector to the interactive tools used in the residential sector (Linné, 2010). Geo-information and GIS became a powerful tool in inventory control and decision making in opencast mining (Blachowski et al., 2011). The use of mass valuation approximation for a mining surface cost estimation purposes is understandable and not new but what makes difference is the way of gathering, processing data and its quality and accuracy.

There are many approaches to perform mass valuation (estimation of value), nevertheless with not many information about other properties this approach in this research is limited to calculate their estimated value by multiplying calculated average price for each type of property (agricultural or residential) by its area. To prepare analysis spatial database was created with PostgreSQL and PostGIS as extension use.

Fig. 1. Spatial database structure

In table regbuildings there is a column named NEWPROPERTYVAL in which current values of properties were estimated. Using these values new vector layer was created just to demonstrate land cost distribution. Optimization of open pit mine location was performed using original values from the database. To prepare visualization of land cost layer the Jenks’ Natural Breaks algorithm was used for classifying data presented in a land cost map. The use of this algorithm is a result of uneven distribution of property estimated values which excludes the use of arbitrary equal interval. Jenks’ Natural Breaks is a common method that best represent the actual breaks observed in the data as opposed to some arbitrary classificatory scheme. Algorithm creates k classes so that the variance within groups is minimized. The method was originally published in George Jenks’ Optimal Data Classification for Choropleth Maps (1977).

Fig. 2. Surface cost map of Maniow Maly area - with Jenks’ Natural Breaks classes (Polish currency, zł)

4. SURFACE COST (LAND COST) INFLUENCE ON OUTER DUMP OF WASTE MATERIAL OR OPEN PIT MINE LOCATION In order to investigate how the surface cost can affect the location of outer dump of waste material or planned open pit mine special square grid was created with 125 meter interval. The assumption was made that all real properties that intersects with planned pit are subject of possible acquisition. The grid looks as if it was rectangular but this is a matter of coordinate reference system (WGS 84 – World Geodetic System '84) which was used in this project because of data availability. The analysis were made by summing up the specified parameters from each selection (in grid nodes) of planned pit shape and selected land parcels which at least had one point in common (intersection). The specified parameters are: No. of parcels, total required area,No. of parcels with building(s),total surface cost. Due to predefined grid and constraint that planed pit must be within Maniow Maly area there were 93 nodes from which selections were taken.

5. RESULTS The results are expressed by the basic characteristic like max, min, and average value of parameters collected in tables as well as histograms based upon data of the 93 cases of location. Also two final maps were created presenting two opposite surface cost based locations (max. and min. land cost).

Total surface cost Total required area No. of parcels No. of parcels with building(s) Node

Tab. 2. Collected results based upon 93 cases of location Lowest Max Average Min surface cost [mln zł] 39,76 29,65 15,71 15,71 [km2] 1,61 1,16 0,87 0,98 [-] 208 135,99 55 84 [-] 63 39,43 6 6 [verticalhorizontal]

-

Total No. of nodes Designed pit size or outer dump of waste material

-

-

15 – 8

[-] [km2]

93 0,63

Highest surface cost 39,76 1,55 146 45 10 – 10

All histograms in Fig. 3. were created with 10 equal intervals in range of min-max values of a feature.

7

7 9 7 7

0,87 1,02 1,09 1,17 1,24 1,31 1,39 1,46 1,53 1,61 Wi…

0

Histogram D - No. parcels with building(s) [-] 17 18 14 20 15 8 9 7 7 6 6 10 1 5 0

6,0 17,4 23,1 28,8 34,5 40,2 45,9 51,6 57,3 63,0 W…

Frequency

15… 20… 22… 25… 27… 30… 32… 34… 37… 39… Wi… 1

4

Frequency

20 15 10 5 0

Histogram B - Total required area [km2] 26 30 18 20 12 13 9 6 4 10 1 1 3

Histogram C - No. of parcels [-] 18 16 17

55 85,6 100,9 116,2 131,5 146,8 162,1 177,4 192,7 208 Wi…

Frequency Frequency

20 15 10 5 0

Histogram A - Total surface cost [mln zł] 18 16 11 11 8 8 8 7 5 1

Fig. 3. Set of histograms based upon 93 cases of location

Fig. 4. Two opposite surface cost based locations (max. and min. land cost)

6. CONCLUSIONS Statistics shown in the results section proved the complexity of this topic of optimum outer waste of material or pit location due to vector based surface cost estimation map of land acquisitions and compensations. It is important to note that few things were simplified such as split into rural and urban area, use of grid to create

scenarios and constraint that real property intersected by designed outer dump of waste material or pit cannot be sold as parts. There were 93 scenarios instead of unlimited possibilities of locating planned pit inside Maniow Maly area. The most interesting statistic like total surface cost was bounded by 15,71 – 39,76 mln zł range whereas total land area was bounded by 0,87 – 1,61 km2 range. The more land parcels future mine intersects the more possible conflicts may occur. Extreme node selection pointed out 208 land parcels for buyout where it could be 55 in other case. Vector based surface cost map model has more advantages in comparison to block model because in block model total surface cost is systematically under estimated due to not considering real estate’s as integral parts. Also there is no straight answer what size of these blocks would be appropriate.

REFERENCES ASKARI-NASAB, H., AWUAH-OFFEI, K. Mining Technology. Mar2009, Vol. 118 Issue 1, 1-12 BLACHOWSKI, J., GÓRNIAK-ZIMROZ, J., JURDZIAK, L., KAWALEC, W., PACTWA, K., SPECYLAK-SKRZYPECKA, J., ŚLUSARCZYK, G., Structure of the rock mineral deposits geoinformation system – assumptions, Prace Naukowe Instytutu Górnictwa Politechniki Wrocławskiej. Studia i Materiały, Vol. 132, No. 39, 2011, 23–34 INTERNATIONAL VALUATION STANDARDS, Polish edition, Polska Federacja Stowarzyszeń Rzeczoznawców Majątkowych, 2005, 265-267. KHALOKAKAIE R, P. A. DOWD, R. J. FOWELL, Lerchs–Grossmann algorithm with variable slope angles, Mining Technology, Vol. 109, No. 2, 2000, 77–89 LINNÉ M., R., CIRINCIONE J., Integrating Geographic Information and Valuation Modeling for Real Estate, The Appraisal Journal, Fall 2010, 370-378. REAL ESTATE TURNOVER 2010, Statistical Information and Elaborations, Warsaw, 2011, 162–164. SAWIŁOW E., Ocena algorytmów wyceny nieruchomości w podejściu porównawczym, Journal Of The Polish Real Estate Scientific Society vol. 19 no. 3, Olsztyn, 2011, 20–32.

Optymalizacje w górnictwie, koszty powierzchniowe, wycena, GIS, wykup TWORZENIE MODELI KOSZTÓW POWIERZCHNIOWYCH DLA KOPALŃ ODKRYWKOWYCH Z WYKORZYSTANIEM NARZĘDZI GIS Górnictwo odkrywkowe dzięki różnym typom kopalin i wielkości złóż w różny sposób oddziałuje na powierzchnię terenu, począwszy od małych wyrobisk do wielohektarowych kopalni węgla brunatnego. Koszty powierzchniowe w górnictwie odkrywkowym stały się tematem coraz częściej poruszanym ze względu na wysokie koszty pozyskania nieruchomości i postępującej urbanizacji. Artykuł ukazuje różnicę pomiędzy wektorowym a blokowym modelem kosztów powierzchniowych. Stworzono przestrzenną bazę danych dla analiz geometrii modelu kosztów powierzchniowych w odniesieniu do potencjalnej lokalizacji zwałowiska zewnętrznego bądź wyrobiska o zadanym kształcie. Nowo powstałe modele wektorowe mogą zwiększyć dokładność oszacowania kosztów związanych z wykupem nieruchomości w początkowej fazie inwestycji surowcowych.